Stormwater discharges from municipalities are regulated by provisions in the Clean Water Act of 1972 to protect the Nation’s water resources from harmful pollutants. In 2014, the Kansas Department of Health and Environment issued new stormwater discharge permits for 17 municipalities in Johnson County, Kansas, in the northeastern part of the State. The county is largely suburban and has 20 municipalities within 22 watersheds. Municipalities in Johnson County are required to implement stormwater management programs that reduce discharges of pollutants, protect water quality, and satisfy applicable water-quality regulations.
In 2015, the U.S. Geological Survey, in cooperation with the Johnson County Stormwater Management Program, began a 4-year monitoring program designed to meet new stormwater monitoring requirements for some municipalities in Johnson County. Additional data were collected to evaluate the usefulness of continuous water-quality monitoring and different sampling methods in assessing changes in water quality. Twelve of the 22 watersheds in the county were within the sampling network for this project.
Discrete water-quality samples were collected at 25 stream sites and 2 lake sites using passive, grab, and equal-width increment sampling methods. Samples at all sites were analyzed for nutrients, Escherichia coli bacteria, total suspended solids, and suspended-sediment concentration. Ninety-nine percent of storm-event samples and 98 percent of low-flow samples were less than the Kansas Surface Water Quality Standard for nitrate plus nitrite. Eight percent of storm-event samples and 100 percent of low-flow samples were less than the total suspended solids screening value of 50 milligrams per liter. Passive samples generally had higher concentrations when compared to equal-width increment and grab samples, and grab samples and equal-width increment samples generally had similar concentrations.
Continuous water-quality data were collected at one site. Ordinary least squares regression analysis was used to relate continuous (15-minute) water-quality sensor measurements to discretely sampled constituent concentrations at one site.
Numerous factors affect water quality in urban runoff. Urban areas have many possible contaminant sources, including municipal and industrial wastewater discharges, stormwater runoff from impervious surfaces, and failing infrastructure. A better understanding of these factors can inform future monitoring efforts, leading to datasets that are representative of storm runoff and can be used to detect differences between sites and over time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215041","collaboration":"Prepared in cooperation with the Johnson County Stormwater Management Program","usgsCitation":"Leiker, B.M., Rasmussen, T.J., Eslick-Huff, P.J., and Painter, C.C., 2021, Assessment of water-quality constituents monitored for total maximum daily loads in Johnson County, Kansas, January 2015 through December 2018: U.S. Geological Survey Scientific Investigations Report 2021–5041, 45 p., https://doi.org/10.3133/sir20215041.","productDescription":"Report: viii, 45 p.; Appendixes: 62 p.; Data Release","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-119343","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":387659,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91397BC","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality and preceding precipitation data for low-flow and storm-event samples collected in Johnson County, Kansas, from January 2015 through November 2018"},{"id":387657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5041/sir20215041.pdf","text":"Report","size":"3.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5041"},{"id":387656,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5041/coverthb.jpg"},{"id":387658,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5041/sir20215041_appendixes_2to6.pdf","text":"Appendixes 2–6","size":"2.27 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5041 Appendixes"}],"country":"United States","state":"Kansas","county":"Johnson County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-94.6075,39.0437],[-94.6075,39.0399],[-94.6082,38.8463],[-94.6084,38.8341],[-94.6102,38.7376],[-95.0572,38.7395],[-95.0558,38.9816],[-95.0477,38.9778],[-95.0383,38.9771],[-95.0312,38.9773],[-95.0292,38.9813],[-95.0271,38.9881],[-95.0249,38.9962],[-95.0189,38.9987],[-95.0135,38.9991],[-95.0077,38.998],[-94.9946,38.9976],[-94.9899,38.997],[-94.9841,38.995],[-94.9789,38.9926],[-94.9755,38.9885],[-94.9704,38.9851],[-94.9645,38.9832],[-94.9575,38.982],[-94.9527,38.9828],[-94.9479,38.9845],[-94.9448,38.9871],[-94.9423,38.9898],[-94.9386,38.9933],[-94.9367,38.9964],[-94.9335,38.9995],[-94.9264,38.9998],[-94.9217,38.9996],[-94.9176,38.9977],[-94.9209,38.9919],[-94.923,38.9856],[-94.9207,38.9837],[-94.9164,38.9859],[-94.9115,38.9889],[-94.9078,38.9924],[-94.9014,39.0022],[-94.8989,39.0053],[-94.8945,39.0102],[-94.8919,39.0155],[-94.891,39.021],[-94.8875,39.0313],[-94.8824,39.0379],[-94.8768,39.0441],[-94.8681,39.052],[-94.8631,39.0564],[-94.8488,39.0578],[-94.8318,39.0546],[-94.8131,39.0486],[-94.8038,39.0456],[-94.7197,39.0435],[-94.6693,39.0433],[-94.6075,39.0437]]]},\"properties\":{\"name\":\"Johnson\",\"state\":\"KS\"}}]}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
The U.S. Geological Survey (USGS), in cooperation with the Montana Department of Transportation (MDT), has operated a crest-stage gage (CSG) network in Montana to collect peak-flow data since 1955. The CSG network is vital to collecting peak-flow data on small drainage basins that typically are not addressed by continuous streamflow operations. Discussions between USGS and MDT identified a need for evaluating the CSG network to allow for better decision making in the management of the network. The purpose of this report is to (1) generally describe peak-flow variability in Montana, (2) assess peak-flow informational needs relevant to MDT activities, and (3) consider the characteristics of the active CSG network in relation to addressing the informational needs. The evaluation of the CSG network is intended to assist in prioritization for discontinuation of CSGs and other activities involving changes to the CSG network.
Peak-flow variability was investigated by analysis of selected peak-flow characteristics of 659 unregulated streamgages in or near Montana. A generalized peak-flow variability index (PFVI) was developed to provide large-scale representation of peak-flow variability in Montana. For unregulated Montana streamgages, PFVI generally monotonically decreases with increasing drainage area, although there is somewhat large (but generally consistent) variability about the locally weighted scatterplot smooth line. Presumably, highly variable small-scale hydroclimatic processes are integrated with increasing drainage area such that variability in many hydrologic characteristics is reduced. PFVI also decreases with increasing mean basin elevation and mean annual precipitation. Presumably, higher elevation and wetter hydroclimatic settings in Montana contribute to reduced variability in hydrologic characteristics. Intuitively, PFVI might be expected to generally decrease with increasing years of record because the standard deviation might typically be expected to decrease with increasing sample size. However, relations among PFVI and years of record are more complex and variable than drainage area, elevation, and precipitation. PFVI variably increases from 10 to about 40 years of record and then generally monotonically decreases from about 40 to about 105 years of record. Relations among PFVI and the years of record might be confounded by effects of drainage area because streamgages with long periods of record (greater than about 60 years) generally have large drainage areas (greater than about 100 square miles).
The relations between PFVI and drainage area, mean basin elevation, mean annual precipitation, and years of record substantially differ among the eight hydrologic regions in Montana. As such, the PFVI relations were further investigated within each hydrologic region.
A major use of peak-flow information by MDT is for design of road and highway infrastructure, including bridges, culverts, and roadside drainage ditches. As such, basin characteristics (including drainage area, mean basin elevation, and mean annual precipitation) of the Montana streamgage network (735 regulated and unregulated streamgages) were statistically investigated in relation to basin characteristics of 12,639 road and stream intersections in Montana. Both regulated and unregulated streamgages were investigated because the road and stream intersections are on both regulated and unregulated streams. Exploratory analyses indicated that the various relations substantially differ among the hydrologic regions. As such, the relations between the Montana streamgage network and the road and stream intersections were further investigated within each hydrologic region.
An important objective of the CSG network is to provide data for developing regional regression equations (RREs) for estimating frequencies at ungaged sites in Montana. Various characteristics of the RREs substantially differ among the eight hydrologic regions in Montana. As such, the RRE characteristics were further investigated within each hydrologic region.
For each of the eight hydrologic regions, various characteristics of peak-flow variability, peak-flow informational needs, and regional regression analyses were investigated in detail. Possible shortcomings of the streamgage network in each hydrologic region are identified and possible future improvements to the CSG network are presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215063","collaboration":"Prepared in cooperation with the Montana Department of Transportation","usgsCitation":"Sando, S.K., 2021, Peak-flow variability, peak-flow informational needs, and consideration of regional regression analyses in managing the crest-stage gage network in Montana: U.S. Geological Survey Scientific Investigations Report 2021–5063, 124 p., https://doi.org/10.3133/sir20215063.","productDescription":"Report: x, 124 p.; Data Release; Dataset","numberOfPages":"138","onlineOnly":"Y","ipdsId":"IP-121407","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":387116,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5063/coverthb.jpg"},{"id":387117,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5063/sir20215063.pdf","text":"Report","size":"14.1 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\"}}]}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Group-size variation is common in colonially breeding species, including seabirds, whose breeding colonies can vary in size by several orders of magnitude. Seabirds are some of the most threatened marine taxa and understanding the drivers of colony size variation is more important than ever. Reproductive success is an important demographic parameter that can impact colony size, and it varies in association with a number of factors, including nesting habitat quality. Within colonies, seabirds often aggregate into distinct groups or subcolonies that may vary in quality. We used data from two colonies of Adélie penguins 73 km apart on Ross Island, Antarctica, one large and one small to investigate (1) How subcolony habitat characteristics influence reproductive success and (2) How these relationships differ at a small (Cape Royds) and large (Cape Crozier) colony with different terrain characteristics. Subcolonies were characterized using terrain attributes (elevation, slope aspect, slope steepness, wind shelter, flow accumulation), as well group characteristics (area/size, perimeter-to-area ratio, and proximity to nest predators). Reproductive success was higher and less variable at the larger colony while subcolony characteristics explained more of the variance in reproductive success at the small colony. The most important variable influencing subcolony quality at both colonies was perimeter-to-area ratio, likely reflecting the importance of nest predation by south polar skuas along subcolony edges. The small colony contained a higher proportion of edge nests thus higher potential impact from skua nest predation. Stochastic environmental events may facilitate smaller colonies becoming “trapped” by nest predation: a rapid decline in the number of breeding individuals may increase the proportion of edge nests, leading to higher relative nest predation and hindering population recovery. Several terrain covariates were retained in the final models but which variables, the shapes of the relationships, and importance varied between colonies.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s41598-021-94861-7","usgsCitation":"Schmidt, A.E., Ballard, G., Lescroël, A., Dugger, K., Jongsomjit, D., Elrod, M.L., and Ainley, D., 2021, The influence of subcolony-scale nesting habitat on the reproductive success of Adélie penguins: Scientific Reports, v. 11, 15380, 15 p., https://doi.org/10.1038/s41598-021-94861-7.","productDescription":"15380, 15 p.","ipdsId":"IP-105335","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":451374,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-021-94861-7","text":"Publisher Index Page"},{"id":388881,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Cape Crozier, Cape Royds, Ross Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 160.1806640625,\n -78.56048828398782\n ],\n [\n 173.32031249999997,\n -78.56048828398782\n ],\n [\n 173.32031249999997,\n -75.28657817848102\n ],\n [\n 160.1806640625,\n -75.28657817848102\n ],\n [\n 160.1806640625,\n -78.56048828398782\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2021-07-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Annie E.","contributorId":265338,"corporation":false,"usgs":false,"family":"Schmidt","given":"Annie","email":"","middleInitial":"E.","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":822578,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ballard, Grant","contributorId":265339,"corporation":false,"usgs":false,"family":"Ballard","given":"Grant","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":822579,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lescroël, Amélie","contributorId":265340,"corporation":false,"usgs":false,"family":"Lescroël","given":"Amélie","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":822580,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":822577,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jongsomjit, Dennis","contributorId":265341,"corporation":false,"usgs":false,"family":"Jongsomjit","given":"Dennis","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":822581,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Elrod, Megan L.","contributorId":265342,"corporation":false,"usgs":false,"family":"Elrod","given":"Megan","email":"","middleInitial":"L.","affiliations":[{"id":48619,"text":"pbcs","active":true,"usgs":false}],"preferred":false,"id":822582,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ainley, David G.","contributorId":265343,"corporation":false,"usgs":false,"family":"Ainley","given":"David G.","affiliations":[],"preferred":false,"id":822583,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70221916,"text":"ofr20211051 - 2021 - Groundwater and surface-water data from the C-aquifer monitoring program, Northeastern Arizona, 2012–2019","interactions":[],"lastModifiedDate":"2021-07-15T10:09:37.240431","indexId":"ofr20211051","displayToPublicDate":"2021-07-14T14:13:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1051","displayTitle":"Groundwater and Surface-Water Data from the C-Aquifer Monitoring Program, Northeastern Arizona, 2012–2019","title":"Groundwater and surface-water data from the C-aquifer monitoring program, Northeastern Arizona, 2012–2019","docAbstract":"The Coconino aquifer (C aquifer) is a regionally extensive multiple-aquifer system supplying water for municipal, agricultural, and industrial use in northeastern Arizona, northwestern New Mexico, and southeastern Utah. This report focuses on the C aquifer in the arid to semi-arid area between St. Johns, Ariz., and Flagstaff, Ariz., along the Interstate-40 corridor where an increase in groundwater withdrawals coupled with ongoing drought conditions increase the potential for substantial water-level decline within the aquifer.
The U.S. Geological Survey (USGS) C-aquifer Monitoring Program began in 2005 to establish baseline groundwater and surface-water conditions and to quantify physical and water-chemistry responses to pumping stresses and climate. This report presents data previously reported in Brown and Macy (2012) that extend back as far as the 1950s, along with new data collected from the USGS C-aquifer Monitoring Program since that publication, from water years 2012 to 2019.
Water levels in 17 wells are measured quarterly as part of the C-aquifer Monitoring Program, and five of those are continuously monitored at 15-minute intervals. Water levels in an additional 18 wells in the study area are measured periodically by the USGS or other agencies. The largest historical change in water level in the study area was a decrease of 81.20 feet in Lake Mary 1 Well near Flagstaff between 1962 and 2018. Changes in water levels were greatest around major pumping centers and in the eastern extent of the study area.
Surface-water water-quality parameters (pH, water temperature, specific conductance, and dissolved oxygen) and streamflow discharge measurements were collected and analyzed along perennial, groundwater-fed reaches of Clear Creek, Chevelon Creek, and the Little Colorado River during nine baseflow investigations of varying extent between 2005 and 2019. Both Clear Creek and Chevelon Creek gain in flow from the beginning of their perennial reaches to their outflow into the Little Colorado River. The Little Colorado River has relatively steady streamflow in the reach between where the two tributaries enter the river. Chevelon Creek showed an increase in median specific conductance during all baseflow investigations of nearly 4,000 microsiemens per centimeter (μS/cm) from near the headwaters to the confluence with the Little Colorado River; Clear Creek also showed an increase in median specific conductance of almost 5,000 μS/cm from headwaters to confluence. Water temperature, dissolved oxygen, and pH do not show substantial trends along the reaches of Clear Creek, Chevelon Creek, or the Little Colorado River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211051","collaboration":"Prepared in cooperation with the Navajo Nation and the City of Flagstaff","usgsCitation":"Jones, C.J.R., and Robinson, M.J., 2021, Groundwater and surface-water data from the C-aquifer monitoring program, Northeastern Arizona, 2012–2019: U.S. Geological Survey Open-File Report 2021–1051, 34 p., https://doi.org/10.3133/ofr20211051.","productDescription":"vi, 34 p.","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-115787","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":387185,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20121196","text":"Open-File Report 2012-1196","linkHelpText":"- Groundwater, Surface-Water, and Water-Chemistry Data from C-aquifer Monitoring Program, Northeastern Arizona, 2005-11"},{"id":387177,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1051/covrthb.jpg"},{"id":387178,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1051/ofr20211051.pdf","text":"Report","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.829833984375,\n 34.27083595165\n ],\n [\n -109.149169921875,\n 34.27083595165\n ],\n [\n -109.149169921875,\n 36.146746777814364\n ],\n [\n -111.829833984375,\n 36.146746777814364\n ],\n [\n -111.829833984375,\n 34.27083595165\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Bedload and ancillary data were collected to calculate and compare the bedload trapping efficiencies of four types of pressure-difference bedload samplers as part of episodic, sediment-recirculating flume experiments at the St. Anthony Falls Laboratory, University of Minnesota, Minneapolis, in January–March 2006. The bedload-sampler experiments, which were conceived, organized, and led by the U.S. Geological Survey’s Office of Surface Water, were part of a broader suite of experiments performed in the rectangular, concrete-lined, sediment-recirculating Main Channel Facility (“main channel flume”). Collectively referred to as “StreamLab06,” the experiments were conducted under the auspices of the National Center for Earth-Surface Dynamics, University of Minnesota.
Four pressure-difference-type bedload samplers—a standard Helley-Smith, US BLH-84, Elwha, and Toutle River-2—were deployed by using hand-held rods in the main flume in a series of trials during steady flows as part of the first two of seven phases of the StreamLab06 experiments. The Phase I flows were released over a sand bed. Gravel composed the bed during the Phase II flows. Bedload samples were collected during flows ranging from 2.0 cubic meters per second (near the incipient motion of bed material) to 5.5 cubic meters per second. A total of 2,030 bedload samples were collected—1,000 as part of 19 sand-bed trials, and 1,030 as part of 27 gravel-bed trials.
Bedload was captured in five contiguous weigh drums inside a slot spanning the full width of the main flume channel 8.5 meters downstream from the cross-section in which the bedload samplers were deployed. The contents of each drum were automatically weighed and recorded as a time series about every 1.1 seconds. Each drum automatically, independently, and episodically dumped its contents into the bottom of the slot upon the accumulation of a pre-determined mass of entrapped sediment, after which the drum continued to capture and weigh bedload. An auger at the bottom of the slot evacuated the accumulating sediment to a side-channel pump that piped the captured sediments upstream and discharged them back to the flume.
Bedload-transport rates were calculated from measurements of the masses of material trapped by the bedload samplers and from the data produced by the automated bedload capture-and-weigh system of the main channel flume. These data were used to compute at-a-point and mean bedload-transport rates for subsequent use in developing bedload-trapping efficiency (calibration) coefficients for each bedload sampler and for comparing the relative trapping efficiencies of the manually deployed bedload samplers. The data were collected to enable the use of several computational methods for deriving bedload-trapping coefficients.
Continuous ancillary data including stage, water discharge, and water temperature were automatically collected and stored. Flow depths were manually measured and recorded concurrent with each at-a-point bedload-sampler deployment. Other information obtained during parts of the experiments included longitudinal water-surface slope, bedload particle-size distributions, and suspended-sediment concentrations and percent sand analyzed from samples collected by depth integration with a US DH-48 isokinetic suspended-sediment sampler.
This report describes the types and availability of the bedload and ancillary data derived through the StreamLab06 experiments. The data are available from the St. Anthony Falls Laboratory and the U.S. Geological Survey through a data release. Also included are selected descriptive and historical information as well as the background, experimental design, experimental caveats, and other factors relevant to the production of the bedload-transport and ancillary data produced through Phases I and II of the StreamLab06 experiments.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211064","usgsCitation":"Gray, J.R., Schwarz, G.E., Dean, D.J., Czuba, J.A., and Groten, J.T., 2021, Instruments, methods, rationale, and derived data used to quantify and compare the trapping efficiencies of four types of pressure-difference bedload samplers: U.S. Geological Survey Open-File Report 2021–1064, 61 p., https://doi.org/10.3133/ofr20211064.","productDescription":"Report: vii, 61 p.; Data Release","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-098017","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":5058,"text":"Office of the Chief Scientist for Water","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386969,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1064/ofr20211064.pdf","text":"Report","size":"70.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1064"},{"id":386970,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1064/coverthb.jpg"},{"id":386971,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VBB2YF","text":"USGS data release","linkHelpText":"Data describing the trapping efficiency of four types of pressure-difference bedload samplers, St. Anthony Falls Laboratory, Minneapolis, Minnesota, 2006"}],"contact":"Chief, Analysis and Prediction Branch
Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Mail Stop 415
Reston, VA 20192
Aquatic connectivity projects, such as removing dams and modifying culverts, have substantial benefits. The restoration of natural flow conditions improves water quality, sediment transport, aquatic and riparian habitat, and fish passage. These projects can also decrease hazards faced by communities by lowering water-surface elevations of flood waters and by removing the risk of dam breaches associated with aging or inadequate infrastructure.
This report documents and provides results of one- and two-dimensional hydraulic models developed for selected rivers and streams in the northeastern United States where a dam was removed or a culvert was retrofitted. The models were developed for conditions before and after the dam removal or culvert modification. The discharges applied in the models included monthly discharges and flood discharges for the annual exceedance probabilities of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent.
This study, by the U.S. Geological Survey in cooperation with the U.S. Fish and Wildlife Service, demonstrates the benefits resulting from dam removal and retrofitting undersized culverts in terms of decreased water-surface elevations during flooding and improved fish passage. The U.S. Army Corps of Engineers Hydrologic Engineering Center’s River Analysis System was used to model the sites in one- and two-dimensional hydraulics, and decreases in the 1-percent annual exceedance probability discharge water-surface elevation were found at all sites studied. The decreases in water-surface elevation at sites in which the impoundment was removed ranged from 1.3 to 10.4 feet. One site, Bradford Dam in Westerly, Rhode Island, had only a 0.2-foot decrease, but at that site the dam was replaced by a series of weirs to retain the upstream impoundment and allow fish passage.
Minimal differences were found between the water-surface elevations computed by the one- and two-dimensional models. The two-dimensional models, however, provide the additional benefit of detailed velocity and depth data throughout the channel at a resolution not possible with a one-dimensional model. These velocity and depth data allowed for assessment of the suitability for fish passage at the sites. Fish passage was improved at all the sites by removing the dams and retrofitting the culvert. Prolonged swim velocity criteria for selected fish species were maintained throughout three of the nine study sites, and burst swim velocity criteria were met at all study sites.
Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop a prototype tool for optimizing tidal marsh management decisions at the Edwin B. Forsythe National Wildlife Refuge in New Jersey. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of 23 marsh management units within the refuge and estimated the outcomes of each action in terms of performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale. Results indicated that, for the objectives and actions considered here, total management benefits may increase consistently up to about \\$980,000, but that further expenditures may yield diminishing return on investment. Potential management actions in optimal portfolios at total costs less than \\$980,000 included applying sediment to the marsh surface to increase elevation in five marsh management units, digging runnels on the marsh surface to improve drainage in five marsh management units, and breaching roads and berms to improve tidal flow in five marsh management units. The potential management benefits were derived from expected reduction in the duration of surface flooding, improved capacity for marsh elevation to keep pace with sea-level rise and increases in numbers of spiders (as an indicator of trophic health), tidal marsh obligate birds, and wintering American black ducks. The prototype presented here does not resolve management decisions; rather, it provides a framework for decision making at the Edwin B. Forsythe National Wildlife Refuge that can be updated as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211037","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., Castelli, P.M., and Rettig, V., 2021, Optimization of salt marsh management at the Edwin B. Forsythe National Wildlife Refuge, New Jersey, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1037, 41 p., https://doi.org/10.3133/ofr20211037.","productDescription":"vi, 41 p.","ipdsId":"IP-120822","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":386007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1037/coverthb.jpg"},{"id":386008,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1037/ofr20211037.pdf","text":"Report","size":"7.86 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1037"},{"id":386009,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1037/images"}],"country":"United States","state":"New Jersey","otherGeospatial":"Edwin B. Forsythe 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 -74.41967010498045,\n 39.388182633584485\n ],\n [\n -74.36851501464844,\n 39.40967202224426\n ],\n [\n -74.36817169189453,\n 39.433011014927224\n ],\n [\n -74.33040618896484,\n 39.45395640766923\n ],\n [\n -74.31255340576172,\n 39.48125549646666\n ],\n [\n -74.3276596069336,\n 39.50059690888215\n ],\n [\n -74.4107437133789,\n 39.51807903374736\n ],\n [\n -74.43305969238281,\n 39.519138415094176\n ],\n [\n -74.4601821899414,\n 39.51198727745152\n ],\n [\n -74.4275665283203,\n 39.49397374330326\n ],\n [\n -74.45743560791016,\n 39.46959506012395\n ],\n [\n -74.44267272949219,\n 39.45766759232811\n ],\n [\n -74.41967010498045,\n 39.388182633584485\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program is an effort to classify, describe, and map existing vegetation communities in national park units throughout the United States. The NPS VMI Program is managed by the NPS Natural Resource Stewardship and Science Inventory and Monitoring Program and provides baseline vegetation information to natural resource managers, researchers, and ecologists. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, and NPS Great Smoky Mountains National Park (GRSM, also referred to as the “Park”) have completed vegetation classification and mapping of GRSM, including the Foothills Parkway, for the NPS VMI Program.
Mappers, ecologists, and botanists collaborated to affirm vegetation types of GRSM and to determine how best to map the vegetation types by using aerial imagery. A vegetation classification developed in 2003 by NatureServe and the NPS served as a foundation to further classify and map the vegetation types of the Park. Data from an additional 10 vegetation plots supported vegetation types either rare or not documented in the 2003 classification. Data from 203 verification sites were collected to test the field key to vegetation types and the application of vegetation types to a sample set of map polygons. Furthermore, data from 972 accuracy assessment (AA) sites were collected (of which 966 were used to test accuracy of the vegetation map layer). This GRSM vegetation mapping project identified 112 vegetation types consisting of 105 association types in the U.S. National Vegetation Classification (USNVC), 2 “park-special” types, 1 “map-special” type, and 4 cultural types in the USNVC.
To map the vegetation and land cover of GRSM, 52 map classes were developed. Of these 52 map classes, 46 represent natural (including ruderal) vegetation types, most of which types are recognized in the USNVC. For the remaining 6 of the 52 map classes, 4 represent USNVC cultural types for agricultural and developed areas, and 2 represent non-USNVC types for nonvegetated open water and nonvegetated rock. Features were interpreted from viewing four-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems; digital aerial imagery was collected during September 23–October 30, 2015. The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in a geographic information system. Polygon units were mapped to either a 0.5- or 0.25- hectare (ha) minimum mapping unit, depending on vegetation type.
A geodatabase containing several feature-class layers and tables provides the locations and data of USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, AA sites, project boundary extent, and aerial image centers and flight lines.
Covering 210,875 ha, the feature-class layer and related tables for the vegetation map layer provide 34,084 polygons of detailed attribute data when special modifiers are not considered (average polygon size of 6.2 ha) and 36,589 polygons of detailed attribute data when special modifiers are considered (average polygon size of 5.8 ha). Each map polygon is assigned a map-class code and name and, when applicable, are linked to USNVC classification tables within the geodatabase. The vegetation map extent includes the administrative boundary for GRSM and the Foothills Parkway.
A summary report, generated from the vegetation map layer, concludes that the 46 map classes representing natural (including ruderal) vegetation types apply to 99.2% of polygons (33,797 polygons; average size of 6.2 ha) and cover 98.6% of the Park (207,971.4 ha). Further broken down, map classes representing natural vegetation types indicate that the Park is 97.7% forest and woodland (205,882.5 ha), 0.6% shrubland (1,174.6 ha), and 0.4% herbaceous (914.3 ha). Map classes representing cultural vegetation types apply to 0.8% of polygons (259 polygons; average size of 4.9 ha) and cover 0.6% of the Park (1,277.4 ha). Map classes representing nonvegetation open and flowing water and unvegetated rock apply to 0.08% of polygons (28 polygons; average size of 58.1 ha) and cover 0.8% of the Park (1,625.9 ha).
A thematic AA study was completed of map classes representing the natural (including ruderal) vegetation types of the Park. Initial AA results were discussed with NPS staff from the Park. Following input from NPS staff on how to handle map classes that fell below accuracy standards, adjustments were made to the vegetation map layer. Final results indicate an overall accuracy of 80.64% (kappa index of 79.96% for chance agreements) based on data from 966 of the 972 AA sites. Most individual map-class themes exceed the NPS VMI Program standard of 80% with a 90% confidence interval.
The GRSM vegetation mapping project delivers many geospatial and vegetation data products, including an in-depth project report discussing methods and results, which includes map classification and map-class descriptions. This suite of products also includes descriptions and a field key to vegetation types; a database of vegetation plots, verification sites, and AA sites; digital images of field sites; field data sheets; digital aerial imagery; hardcopy and digital maps; a geodatabase of vegetation and land cover (map layer), field sites (vegetation plots, verification sites, and AA sites), aerial imagery index, project boundary, and metadata; and a contingency table listing AA results. Geospatial products are projected in the Universal Transverse Mercator, Zone 17 North, by using the North American Datum of 1983. Information on the NPS VMI Program and completed mapping projects are on the internet at https://www.nps.gov/im/vegetation-inventory.htm.
","language":"English","publisher":"National Park Service","doi":"10.36967/nrr-2286888","usgsCitation":"Hop, K.D., Strassman, A.C., Sattler, S., White, R., Pyne, M., Govus, T., and Dieck, J., 2021, National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project: Natural Resource Report 2021/2285, 220 p., https://doi.org/10.36967/nrr-2286888.","productDescription":"220 p.","ipdsId":"IP-120204","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":388150,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Tennessee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.023681640625,\n 35.594785665487244\n ],\n [\n -83.09783935546875,\n 35.806676609227054\n ],\n [\n -83.40545654296875,\n 35.762114795721\n ],\n [\n -83.88336181640625,\n 35.68853320738875\n ],\n [\n -84.034423828125,\n 35.545635932499415\n ],\n [\n -83.90808105468749,\n 35.43605776486772\n ],\n [\n -83.5565185546875,\n 35.39800594715108\n ],\n [\n -83.30657958984375,\n 35.47409160773029\n ],\n [\n -83.023681640625,\n 35.594785665487244\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hop, Kevin D. 0000-0002-9928-4773 khop@usgs.gov","orcid":"https://orcid.org/0000-0002-9928-4773","contributorId":1438,"corporation":false,"usgs":true,"family":"Hop","given":"Kevin","email":"khop@usgs.gov","middleInitial":"D.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821495,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strassman, Andrew C. 0000-0002-9792-7181 astrassman@usgs.gov","orcid":"https://orcid.org/0000-0002-9792-7181","contributorId":4575,"corporation":false,"usgs":true,"family":"Strassman","given":"Andrew","email":"astrassman@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821496,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sattler, Stephanie 0000-0003-4417-2480 ssattler@usgs.gov","orcid":"https://orcid.org/0000-0003-4417-2480","contributorId":191016,"corporation":false,"usgs":true,"family":"Sattler","given":"Stephanie","email":"ssattler@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821497,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Rickie","contributorId":201063,"corporation":false,"usgs":false,"family":"White","given":"Rickie","email":"","affiliations":[],"preferred":false,"id":821498,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pyne, Milo","contributorId":201061,"corporation":false,"usgs":false,"family":"Pyne","given":"Milo","email":"","affiliations":[],"preferred":false,"id":821499,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Govus, Tom","contributorId":264417,"corporation":false,"usgs":false,"family":"Govus","given":"Tom","email":"","affiliations":[{"id":17658,"text":"NatureServe","active":true,"usgs":false}],"preferred":false,"id":821500,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dieck, Jennifer 0000-0002-4388-4534 jdieck@usgs.gov","orcid":"https://orcid.org/0000-0002-4388-4534","contributorId":149647,"corporation":false,"usgs":true,"family":"Dieck","given":"Jennifer","email":"jdieck@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":821501,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223832,"text":"70223832 - 2021 - Toward improved decision-support tools for Delta Smelt management actions","interactions":[],"lastModifiedDate":"2021-09-09T16:00:10.030009","indexId":"70223832","displayToPublicDate":"2021-06-30T10:48:11","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"seriesTitle":{"id":419,"text":"White Paper","active":false,"publicationSubtype":{"id":9}},"title":"Toward improved decision-support tools for Delta Smelt management actions","docAbstract":"The Collaborative Science and Adaptive Management Program (CSAMP) has endorsed a goal of reversing the recent downward trajectory of the Delta Smelt population within 5-10 generations, with the long-term aim of establishing a self-sustaining population. An ambitious agenda of management actions is planned, and more management actions are being considered. This White Paper furthers one of the recommendations in the 2019 Delta Smelt Science Plan – the need to predict the potential ecological effects of taking a management action. Existing statistical models can be highly informative in assessing the response of Delta Smelt to changing system conditions and management actions. However, management actions can shift or alter conditions in ways that models based on analysis of historical data may not be able to represent, and short-term or localized effects may be missed with models designed to assess effects at the population level.
Decision support tools (DSTs) are computer-based tools developed to assist decision-making, often combining computationally intensive analysis and spatial mapping of environmental relationships. DSTs can be used in planning processes that evaluate an array of actions, such as in Structured Decision Making (SDM), where DSTs are needed to compare among alternatives. DSTs can also be used to explore the potential effects of different approaches to implementing management actions. The goal of this White Paper is to identify plausible options for DSTs that could be developed for future use to evaluate management actions that seek to either reverse the decline of Delta Smelt or minimize or mitigate the effects of other water management actions.
Different types of management actions lead to different needs for DSTs. This White Paper was developed using three types of actions currently being considered to enhance the Delta Smelt population: Supplementation with Hatchery Fish, Summer-Fall Habitat, and Food Enhancement actions. These three management actions target different parts of the estuary and different processes, with a variety of possible metrics to gauge performance.
Three DSTs are proposed that collectively address management questions related to the management actions considered, with each requiring a slightly different set of processes to be included and producing an array of outputs at varying spatial and temporal scales: DST 1. Modeling Fish Movement, Survival, and Reproduction Across Their Range. This DST can address management questions that require information about Delta Smelt spatial distribution and movement.
• DST 1 could be used to compare conditions with and without management actions in place, how the management action performs among different types of water years (with varied flow and associated abiotic conditions), and to assess relative change with different variations and strategies of the management actions.
• DST 2. Changes in Habitat Conditions and Delta Smelt Response. This DST is intended to evaluate combinations of conditions that are considered to provide suitable habitat for Delta Smelt, and Delta Smelt response. Delta Smelt habitat is generally described as open water with low salinity (0 to 6), turbidity of at least 12 NTU, suitable temperature conditions, and sufficient food availability to support growth.
• DST 3. Regional Effects of Food Subsidy. This DSTs seeks to evaluate effectiveness of food enhancement actions by providing information on responses of the immediate targets of the action (i.e., phytoplankton or zooplankton) and tracing those to projected growth responses of Delta Smelt.
There is not a single DST that adequately addresses management questions relevant to all management actions, although there is some overlap in the management questions each of the three DSTs can address.
For each of the DSTs a substantial foundation of models and approaches already exists and modeling has already been applied to several of the management actions described. However, a number of outstanding issues remain for further development of the proposed DSTs. These are summarized in this White Paper together with potential approaches that could be applied or tested. Some components for the DSTs are already available and thus development could be relatively easy. However, for several of the topics identified there are gaps in knowledge that currently limit formulation of model structure and process representations. This presents challenges to readily incorporate some needed mechanisms into the models.
Eleven next steps, aligned with relevant DSTs, are outlined. The next steps vary in their complexity or technical ‘lift’ required. Many build on existing work, or methods and approaches that have already been developed or are underway, while others require additional thinking to establish a viable approach. Some interim utility for decisions could be gained during initial development of the DSTs with further features added over time.
Development of a DST requires engagement of both managers and scientists. Identifying the outputs and resolution needed for management purposes early in development of any DST is essential for effective pursuit of next steps and suitable approaches to address challenges. Dialog between managers and technical experts also informs what process-based simulation can do, and what tradeoffs are acceptable to meet a given purpose. To further develop the DSTs outlined here for application in the estuary requires:
- Engagement of a committed group of technical experts with appropriate expertise.
- The development of a coordinated workplan including appropriate project management and tracking.
- Dialog between potential users (i.e., managers and policy makers) and technical experts.
- Resources to pursue DST development including personnel and computational resources.
This White Paper demonstrates the potential for moving toward DSTs for a variety of management actions in support of Delta Smelt that include mechanistic representations of physical and biological processes. Through focused effort from technical experts, managers and policy makers, DSTs can be developed to provide quantitative predictions of management effects on the ecosystem, targeting the changes the management actions seek to achieve, how these effects compare to ambient conditions, and how the effects vary among water year types or with timing and location of actions. Importantly, solid foundations exist which can be leveraged, refined, and built upon to specifically inform current and future management decisions.
","language":"English","publisher":"Collaborative Adaptive Management Team","usgsCitation":"Reed, D., Acuna, S., Ateljevich, E., Brown, L.R., Geske, B., Gross, E., Hobbs, J., Kimmerer, W.J., Lucas, L., Nobriga, M., and Rose, K.A., 2021, Toward improved decision-support tools for Delta Smelt management actions: White Paper, v, 34 p.","productDescription":"v, 34 p.","ipdsId":"IP-127826","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":389005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389004,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.baydeltalive.com/CSAMP/docs/24756"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reed, Denise","contributorId":215697,"corporation":false,"usgs":false,"family":"Reed","given":"Denise","affiliations":[{"id":37245,"text":"University of New Orleans","active":true,"usgs":false}],"preferred":false,"id":822849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acuna, Shawn","contributorId":257756,"corporation":false,"usgs":false,"family":"Acuna","given":"Shawn","email":"","affiliations":[{"id":52106,"text":"Metropolitan Water District of Southern California","active":true,"usgs":false}],"preferred":false,"id":822850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ateljevich, Eli","contributorId":187437,"corporation":false,"usgs":false,"family":"Ateljevich","given":"Eli","email":"","affiliations":[],"preferred":false,"id":822851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brown, Larry R. 0000-0001-6702-4531 lrbrown@usgs.gov","orcid":"https://orcid.org/0000-0001-6702-4531","contributorId":1717,"corporation":false,"usgs":true,"family":"Brown","given":"Larry","email":"lrbrown@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":822852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Geske, Ben","contributorId":265520,"corporation":false,"usgs":false,"family":"Geske","given":"Ben","email":"","affiliations":[{"id":54715,"text":"Delta Science Program","active":true,"usgs":false}],"preferred":false,"id":822853,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gross, Edward","contributorId":264402,"corporation":false,"usgs":false,"family":"Gross","given":"Edward","affiliations":[{"id":28024,"text":"UCDavis","active":true,"usgs":false}],"preferred":false,"id":822854,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hobbs, Jim","contributorId":200389,"corporation":false,"usgs":false,"family":"Hobbs","given":"Jim","email":"","affiliations":[],"preferred":false,"id":822855,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kimmerer, Wim J.","contributorId":59169,"corporation":false,"usgs":false,"family":"Kimmerer","given":"Wim","email":"","middleInitial":"J.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":822856,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lucas, Lisa 0000-0001-7797-5517 llucas@usgs.gov","orcid":"https://orcid.org/0000-0001-7797-5517","contributorId":260498,"corporation":false,"usgs":true,"family":"Lucas","given":"Lisa","email":"llucas@usgs.gov","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":822857,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nobriga, Matthew","contributorId":139247,"corporation":false,"usgs":false,"family":"Nobriga","given":"Matthew","affiliations":[{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":822858,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rose, Kenneth A","contributorId":147274,"corporation":false,"usgs":false,"family":"Rose","given":"Kenneth","email":"","middleInitial":"A","affiliations":[{"id":16815,"text":"Dept. of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge","active":true,"usgs":false}],"preferred":false,"id":822859,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70221689,"text":"sir20215054 - 2021 - Estimating flow-duration statistics and low-flow frequencies for selected streams and the implementation of a StreamStats web-based tool in Puerto Rico","interactions":[],"lastModifiedDate":"2021-06-29T14:33:40.229468","indexId":"sir20215054","displayToPublicDate":"2021-06-28T16:46:42","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5054","displayTitle":"Estimating Flow-Duration Statistics and Low-Flow Frequencies for Selected Streams and the Implementation of a StreamStats Web-Based Tool in Puerto Rico","title":"Estimating flow-duration statistics and low-flow frequencies for selected streams and the implementation of a StreamStats web-based tool in Puerto Rico","docAbstract":"Daily mean streamflow data from 28 U.S. Geological Survey streamflow-gaging stations in Puerto Rico with 10 or more years of unregulated or minimally affected flow record through water year 2018 were used to develop regression equations for flow duration and annual n-day low-flow statistics. Ordinary least-squares and generalized least-squares regression techniques were used to develop regional regression equations for flow-duration statistics at the 99th, 98th, 95th, 90th, 80th, 70th, 60th, and 50th percent exceedance probabilities and annual n-day low-flow frequency statistics for the 1-, 7-, 14-, and 30-day mean low flows with the 2-year (0.5 nonexceedance probability), 5-year (0.2 nonexceedance probability), and 10-year (0.1 nonexceedance probability) recurrence intervals. A StreamStats web application was developed to estimate basin and climatic characteristics for the regional regression equation analysis. Basin and climatic characteristics determined to be significant explanatory variables in one or more regression equations included drainage area, mean total annual reference evapotranspiration, and minimum basin elevation. The adjusted coefficient of determination for the flow-duration regression equations ranged from 57.7 to 81.4 percent. The pseudo coefficient of determination for the annual n-day low-flow regression equations ranged from 64.6 to 70.7 percent. The StreamStats web application incorporates the flow duration, and annual n-day low-flow regression equations and can provide streamflow estimates for most ungaged sites in the island.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215054","collaboration":"Prepared in cooperation with the Puerto Rico Environmental Quality Board","usgsCitation":"Williams-Sether, T., 2021, Estimating flow-duration statistics and low-flow frequencies for selected streams and the implementation of a StreamStats web-based tool in Puerto Rico: U.S. Geological Survey Scientific Investigations Report 2021–5054, 18 p., https://doi.org/10.3133/sir20215054.","productDescription":"Report: v, 17 p.; Data Release; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-118184","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386816,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5054/coverthb.jpg"},{"id":386817,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5054/sir20215054.pdf","text":"Report","size":"5.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5054"},{"id":386819,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":386818,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y2QVJ6","text":"USGS data release","linkHelpText":"Data files for the development of regression equations for flow-duration statistics and n-day low-flow frequencies for ungaged streams in Puerto Rico through water year 2018"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3187255859375,\n 17.85590441431915\n ],\n [\n -65.5828857421875,\n 17.85590441431915\n ],\n [\n -65.5828857421875,\n 18.557739984085266\n ],\n [\n -67.3187255859375,\n 18.557739984085266\n ],\n [\n -67.3187255859375,\n 17.85590441431915\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
A study was conducted by the U.S. Geological Survey in cooperation with the U.S. Navy (the Navy) to determine the status of volatile organic compounds (VOCs) and per- and polyfluoroalkyl substances (PFASs) in groundwater at the former Naval Air Warfare Center (NAWC) in West Trenton, New Jersey. Wells contaminated with VOCs were sampled in 2014, 2015, 2016, and 2017 as part of the Navy’s long-term monitoring program. The results for trichloroethene (TCE), cis-1,2-dichloroethene (cisDCE), and vinyl chloride (VC) were plotted in map view to determine whether the areal extent of the contamination had changed over the 4-year period. TCE, cisDCE, and VC concentrations were plotted along nine lines of section across the former NAWC site to determine whether the vertical distribution of VOCs had changed during 2014–17. TCE, cisDCE, and VC concentrations over time were plotted on graphs for each well to determine long-term trends and changes in VOC concentrations. Data from 1990 to 2017 were used, if available, to make these graphs.
Results show that the areas of VOC concentrations greater than or equal to 1 microgram per liter decreased slightly on the northwestern side and the northeastern side of the NAWC site from 2014 to 2017 under the influence of a pump-and-treat system, natural attenuation processes, and engineered bioaugmentation experiments ongoing at the site. The pump-and-treat system continued to hydraulically contain the VOC contamination and kept it from moving offsite to the south and west of NAWC. One well northeast of the NAWC site, 50BR, was found to have detectable TCE and cisDCE concentrations. These detections indicated that VOC contamination had migrated offsite and that the pump-and-treat system was not containing the VOC contamination on the eastern side of the facility. Detectable VOC concentrations were present in wells as deep as 200 and 221 feet on the eastern and western sides of the NAWC site. TCE concentrations in most wells were found to be stable or to have slowly decreased since the facility closed in 1999. Only 7 wells, including 3 pump-and-treat extraction wells, showed substantial increases in TCE concentration from 2014 to 2017. Continuing sources of TCE to the system are desorption of TCE from organic materials in the aquifer, back diffusion of TCE from the contaminated bedrock matrix, and dissolution of remaining dense nonaqueous phase TCE in the aquifer.
Wells at the former NAWC site were sampled for PFASs in 2015, 2016, and 2017. Perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) results were plotted in map and cross-section views to determine the areal and vertical extent of the PFAS contamination at the site. PFOS, PFOA, and PFNA concentrations greater than their established maximum contaminant levels were detected in 25, 24, and 21 of the 26 wells sampled, respectively, on the eastern side of NAWC in 2017. Vertically, the highest PFAS concentrations were present in shallow wells along the fence near the firehouse and along the railroad tracks where the aqueous film-forming foam discharge reportedly occurred back in 1990. PFAS concentrations were detected in one well (54BR) as deep as 200 feet on the eastern side of the NAWC site. PFASs were present in wells east of the railroad tracks, indicating that PFAS-contaminated groundwater had moved offsite. In a limited test of five wells, samples collected with regenerated cellulose dialysis membrane (RCDM) passive samplers contained PFAS concentrations equal to those in samples from low-flow purging.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201105","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Imbrigiotta, T.E., and Fiore, A.R., 2021, Distribution of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in monitoring wells at the former Naval Air Warfare Center, West Trenton, New Jersey, 2014–17: U.S. Geological Survey Open-File Report 2020–1105, 107 p., https://doi.org/10.3133/ofr20201105.","productDescription":"Report: xii, 107 p.; Data Release; 4 Appendixes","numberOfPages":"107","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-110205","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":386575,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix2.xlsx","text":"Appendix 2","size":"288 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Appendix 2. Volatile organic compounds, per- and polyfluoroalkyl substances, and 1,4-dioxane concentrations measured in samples from wells at the former Naval Air Warfare Center site, West Trenton, New Jersey, 1990–2017"},{"id":386577,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix2.csv","text":"Appendix 2 as CSV file","size":"187 KB","linkFileType":{"id":7,"text":"csv"}},{"id":386576,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix1.csv","text":"Appendix 1 as CSV file","size":"22.9 KB","linkFileType":{"id":7,"text":"csv"}},{"id":386573,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCAQ5N","text":"USGS data release","linkHelpText":"Concentrations of chlorinated volatile organic compounds and per- and polyfluoroalkyl substances in groundwater and surface water, former Naval Air Warfare Center, West Trenton, New Jersey"},{"id":386572,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105.pdf","text":"Report","size":"9.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1105"},{"id":386571,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1105/coverthb.jpg"},{"id":386574,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1105/ofr20201105_appendix1.xlsx","text":"Appendix 1","size":"43.7 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Appendix 1. Descriptions of boreholes, well locations, and well construction at the former Naval Air Warfare Center, West Trenton, New Jersey"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.80979204177856,\n 40.26746805544402\n ],\n [\n -74.80759263038635,\n 40.27155298671227\n ],\n [\n -74.8130750656128,\n 40.27224060619094\n ],\n [\n -74.81433033943176,\n 40.26832763061523\n ],\n [\n -74.81412649154663,\n 40.268139343654944\n ],\n [\n -74.80979204177856,\n 40.26746805544402\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
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Lawrenceville, New Jersey, 08648
The U.S. Geological Survey, in cooperation with the U.S. Air Force Civil Engineering Center, investigated the use of surface geophysical methods to delineate the top of the Cretaceous Pierre Shale along survey transects in selected areas within and near Ellsworth Air Force Base, South Dakota. Two complementary geophysical methods—electrical resistivity and passive seismic—were used along 26 co-located transect surveys within and near Ellsworth Air Force Base for a total of 12.7 line-kilometers. Electrical resistivity results were analyzed using EarthImager2D electrical resistivity tomography processing and inversion software. Two-dimensional earth models showing the electrical properties of the subsurface were evaluated by directly comparing the high and low subsurface resistivity values to a surficial geologic map and nearby wells with driller logs. Passive seismic data were analyzed using the horizontal-to-vertical spectral ratio method to determine the depth to the Pierre Shale at each survey point. The electrical resistivity and passive seismic results were compared to driller logs from nearby wells to delineate the top of the Pierre Shale. The depth to the Pierre Shale along the transects ranged from about 2.4 to 20.3 meters, and mean and median depths were about 9.2 and 9.0 meters, respectively. The elevation of the Pierre Shale and thickness of unconsolidated deposits generally increased with land-surface elevation from south to north; however, some transects displayed topographically high and low areas that sometimes did not correlate with land-surface topography and may affect local groundwater flow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3474","collaboration":"Prepared in cooperation with U.S. Air Force Civil Engineering Center","usgsCitation":"Medler, C.J., and Anderson, T.M., 2021, Delineating the Pierre Shale from geophysical surveys within and near Ellsworth Air Force Base, South Dakota, 2019: U.S. Geological Survey Scientific Investigations Map 3474, 3 sheets, 16-p. pamphlet, https://doi.org/10.3133/sim3474.","productDescription":"Pamphlet: ix,16 p.; 3 Sheets: 48.00 x 40.00 inches or smaller; Data Release; Dataset","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-126004","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_pamphlet.pdf","text":"Pamphlet","size":"2.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Pamphlet"},{"id":386682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3474/coverthb2.jpg"},{"id":398136,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3474/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":398000,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3474/images"},{"id":386688,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"},{"id":386687,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJH17","text":"USGS data release","linkHelpText":"Electrical Resistivity Tomography (ERT) and Horizontal-to-Vertical Spectral Ratio (HVSR) data collected within and near Ellsworth Air Force Base, South Dakota, from 2014 to 2019"},{"id":386686,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_sheet03.pdf","text":"Sheet 3","size":"9.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Sheet 3","linkHelpText":"— Electrical resistivity tomography inversion results with depth to Pierre Shale from horizontal-to-vertical spectral ratio results for transects 9A, 9B, 9C, 11, 8A, 8B, 8C, 10, and 12, Ellsworth Air Force Base, South Dakota"},{"id":386685,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_sheet02.pdf","text":"Sheet 2","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Sheet 2","linkHelpText":"— Electrical resistivity tomography inversion results with depth to Pierre Shale from horizontal-to-vertical spectral ratio results for transects 4A1, 4A2, 2, 3A, 3B, 3C, and 5, Ellsworth Air Force Base, South Dakota"},{"id":386684,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3474/sim3474_sheet01.pdf","text":"Sheet 1","size":"8.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3474 Sheet 1","linkHelpText":"— Electrical resistivity tomography inversion results with depth to Pierre Shale from horizontal-to-vertical spectral ratio results for transects 1C1, 1C2, 14, 15, 13A, 13B, 1A, 1B, 4B, and 4C, Ellsworth Air Force Base, South Dakota"},{"id":397999,"rank":8,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3474/sim3474.XML"}],"country":"United States","state":"South Dakota","otherGeospatial":"Ellsworth Air Force Base","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.14857482910155,\n 44.10977494207831\n ],\n [\n -103.04145812988281,\n 44.10977494207831\n ],\n [\n -103.04145812988281,\n 44.17136989600329\n ],\n [\n -103.14857482910155,\n 44.17136989600329\n ],\n [\n -103.14857482910155,\n 44.10977494207831\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
A two-dimensional hydraulic model; water-surface profiles; and digital maps of water-surface elevation, velocities, and water depths were developed for a 6.7-mile reach of Joachim Creek within and near the city of De Soto, Missouri. Water-surface profiles were generated for the 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probability (10-, 25-, 50-, 100-, and 500-year recurrence interval) flows. Digital maps of water-surface elevation, water depth, and velocity were generated for the 1- and 0.2-percent annual exceedance probability flows. Water-surface elevations and inundation extents of generated profiles and maps were substantially lower than similar products produced for the 2019 flood-insurance study that included the study reach. The differences in water-surface elevations can be attributed to differences in input streamflows and hydraulic simulation techniques.
The water-surface elevations generated for the 1- and 0.2-percent annual exceedance probability flows were used to assess the vulnerability and inundation depths of 231 selected structures within the city of De Soto. Results indicate that 157 to 177 of the 231 structures were affected at the 1-percent annual exceedance probability flow, depending on the adjacent grade elevation used for reference. Between 185 and 198 structures were affected at the 0.2-percent annual exceedance probability flow, depending on grade elevation. Inundation depths at the affected structures were 0.02 to 9.28 feet (ft), depending on the flow and adjacent grade reference.
Flood elevations were computed for Joachim Creek using a two-dimensional, finite-volume numerical modeling application for river hydraulics. The hydraulic model was calibrated using high-water marks from the April 18, 2013, flood and the maximum measured streamflow at the U.S. Geological Survey streamgage Joachim Creek at De Soto, Mo. (station 07019500), on September 8, 2018. The calibrated model was then used to compute the hydraulic conditions associated with the 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probability flows. The simulated water-surface elevations and digital elevation model (derived from light detection and ranging data having a 0.60-ft vertical accuracy and a 1.97-ft horizontal resolution) were used to generate products including water-surface profiles and maps of inundated area, water depth, and velocities using model postprocessing software.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215058","collaboration":"Prepared in cooperation with the City of De Soto, Missouri","usgsCitation":"Hix, K.D., Rydlund, P.H., and Heimann, D.C., 2021, Two-dimensional hydraulic analyses of Joachim Creek, De Soto, Missouri: U.S. Geological Survey Scientific Investigations Report 2021–5058, 28 p., https://doi.org/10.3133/sir20215058.","productDescription":"Report: viii, 28 p.; Appendix; 2 Data Releases; Dataset","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-124332","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386714,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":386713,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92MQYE7","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial data and model archive associated with the two-dimensional hydraulic analysis of Joachim Creek, De Soto, Missouri"},{"id":386712,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ACXXI7","text":"USGS data release","description":"USGS data release","linkHelpText":"PeakFQ software input files and selected output files for selected long-term streamgages near Jefferson County, Missouri, through water year 2019"},{"id":386711,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5058/sir20215058_table1.1.csv","text":"Table 1.1 (.csv format)","size":"18.2 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2021–5058 Appendix 1.1","linkHelpText":"— Summary of water-surface elevations and depths at selected structures in the city of De Soto, Missouri, for 1- and 0.2-percent annual exceedance probability streamflows"},{"id":386710,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5058/sir20215058_table1.1.xlsx","text":"Table 1.1 (.xlsx format)","size":"34.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2021–5058 Appendix 1.1","linkHelpText":"— Summary of water-surface elevations and depths at selected structures in the city of De Soto, Missouri, for 1- and 0.2-percent annual exceedance probability streamflows"},{"id":386709,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5058/sir20215058.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5058"},{"id":386708,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5058/coverthb.jpg"}],"country":"United States","state":"Missouri","county":"Jefferson County","otherGeospatial":"Joachim 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Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Increasing demand for the limited water resources of the United States continues to put pressure on resource management agencies to balance the competing needs of ecosystem health with municipal, agricultural, and other uses. To meet these needs, the U.S. Geological Survey conducted a multiyear study to evaluate water resources in the upper Rio Grande Basin in the southwestern United States. The upper Rio Grande Basin extends from south-central Colorado, through New Mexico, into west Texas near Fort Quitman, including parts of Chihuahua, Mexico. The upper Rio Grande Basin consists of a sequence of alluvial basins that formed in the Rio Grande rift approximately 30 million years ago.
This report describes the hydrogeology of the upper Rio Grande Basin and how the groundwater resources in the basin have changed from 1980 to 2015. The hydrogeologic framework includes the horizontal delineation of the alluvial basins within the upper Rio Grande Basin from the headwaters in Colorado to Fort Quitman, Texas, including part of Mexico. Groundwater-level measurements from existing State and Federal data were used to construct groundwater-level altitude and groundwater-level change maps.
Of the 2,699 wells with groundwater-level data used in this study, 1,055 wells had data for only a single 5-year period, 703 wells had data for 50 percent or more of the 35 years of the study, and only 57 wells have 5-year groundwater-level data for the entire study period. The median decline in water levels in the upper Rio Grande Basin was 0.13 foot (ft) per 5-year period, and declines were measured in 53 percent of the 703 wells that contained data for 50 percent or more of the study period. Rates of groundwater-level decline greater than 1 ft per 5-year period were measured in 17 percent of the wells, greater than 2 ft per 5-year period, in 3 percent of the wells, and greater than 3 ft per 5-year period, in 1 percent of the wells. Overall, groundwater levels rose in 6 percent of the 703 wells that contained data for 50 percent or more of the study period, and in 4 percent of the wells, groundwater levels rose by 1 ft or more per 5-year period.
Groundwater-level changes in wells with consecutive 5-year measurement periods exhibited the most variability in the Española, Middle Rio Grande, and Mesilla/Conejos-Médanos alluvial basins. The largest declines in groundwater-level altitudes in individual wells were observed in the Española alluvial basin during 1995–2000, in the Palomas alluvial basin during 2010–2015, and in the Jornada del Muerto alluvial basin during 2005–10. The largest rises in groundwater-level altitudes in individual wells were observed in the Española alluvial basin during 2005–10, in the Middle Rio Grande alluvial basin during 1995–2000, and in the Mesilla/Conejos-Médanos alluvial basin during 1980–85.
Changes in groundwater storage throughout the study period varied by alluvial basin, likely based largely on changes in groundwater withdrawals because of increased demands during drier periods and population growth. All alluvial basins except the Tularosa-Hueco alluvial basin were evaluated for changes in groundwater storage from 1980 to 2015. Extremely limited data availability in 2010–15 for the Tularosa-Hueco alluvial basin led to this 5-year period being dropped from the groundwater-level change map and storage analysis for this basin.
In the San Luis Valley in southern Colorado, efforts to reverse groundwater depletion in the unconfined aquifer recovered approximately 250,000 acre-feet in storage between late 2013 and early 2018, following the implementation of a “pay-to-pump” groundwater program. However, severe drought that persists in the upper Rio Grande Basin, particularly in southern Colorado, has undone some of the conservation efforts. Within the Española alluvial basin, groundwater storage varied because municipal demand increased the demand on groundwater resources and conservation efforts were implemented. A groundwater-flow model evaluated for the Española alluvial basin indicated declines in groundwater storage from 1947 through 1982. Groundwater storage decreased in the Española alluvial basin in 1980–85, 1985–90, 1990–95, 1995–2000, and 2005–10 and increased in 2000–05 and 2010–15 leading to groundwater storage in 2015 about even with that in 1985.
Based on gridded groundwater-level altitudes, groundwater storage decreased in the Middle Rio Grande Basin from 1980 to 2015, except for during the 1980–85, 2000–05, and 2010–15 periods with an overall cumulative storage decrease from 1980 to 2015. Groundwater-flow models evaluated for the Middle Rio Grande alluvial basin showed groundwater storage in the Middle Rio Grande alluvial basin has been reduced since the mid-1950s through the end of the study period except for a brief recovery (reduction in storage outflow) in the mid-1980s. Simulated groundwater storage has also decreased in parts of the Palomas and Mesilla/Conejos-Médanos alluvial basins, and the northern part of the Conejos-Médanos alluvial basin starting in 1995 (excluding 2005 and 2007) and in the Tularosa-Hueco alluvial basin from the early 1940s to the end of the study period. Groundwater storage increased in the Mesilla/Conejos-Médanos alluvial basin during 1980–85 and slightly during 1990–95 and then decreased in the other 5-year periods. Groundwater storage in the Tularosa-Hueco alluvial basin increased from 1985 to 1990, but otherwise decreased, leading to an overall net groundwater-level decline in this part of the basin from 1980 to 2010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215035","programNote":"Water Availability and Use Science Program","usgsCitation":"Houston, N.A., Thomas, J.V., Foster, L.K., Pedraza, D.E., and Welborn, T.L., 2021, Hydrogeologic framework and groundwater characterization in selected alluvial basins in the upper Rio Grande basin, Colorado, New Mexico, and Texas, United States, and Chihuahua, Mexico, 1980 to 2015: U.S. Geological Survey Scientific Investigations Report 2021–5035, 71 p., https://doi.org/10.3133/sir20215035.","productDescription":"Report: viii, 71 p.; Data Release","numberOfPages":"71","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094878","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":436289,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XSJH17","text":"USGS data release","linkHelpText":"Electrical Resistivity Tomography (ERT) and Horizontal-to-Vertical Spectral Ratio (HVSR) Data Collected Within and Near Ellsworth Air Force Base, South Dakota, from 2014 to 2019"},{"id":386627,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7N58KBS","text":"USGS data release","linkHelpText":"Hydrogeologic, geologic, and water-level data for the groundwater component of the upper Rio Grande Focus Area Study, Colorado, New Mexico, and Texas, United States and Chihuahua, Mexico 2017"},{"id":386626,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5035/sir20215035.pdf","text":"Report","size":"22.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5035"},{"id":386625,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5035/coverthb.jpg"}],"country":"Mexico, United States","state":"Colorado, New Mexico, Texas, Chihuahua","otherGeospatial":"Upper Rio Grande Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.5341796875,\n 36.94989178681327\n ],\n [\n -107.9736328125,\n 35.67514743608467\n ],\n [\n -107.75390625,\n 33.358061612778876\n ],\n [\n -107.9736328125,\n 31.728167146023935\n ],\n [\n -107.6220703125,\n 30.524413269923986\n ],\n [\n -106.787109375,\n 29.611670115197377\n ],\n [\n -105.9521484375,\n 29.38217507514529\n ],\n [\n -105.3369140625,\n 30.372875188118016\n ],\n [\n -105.6884765625,\n 31.615965936476076\n ],\n [\n -105.4248046875,\n 33.063924198120645\n ],\n [\n -104.80957031249999,\n 35.53222622770337\n ],\n [\n -104.853515625,\n 38.09998264736481\n ],\n [\n -106.5673828125,\n 38.75408327579141\n ],\n [\n -107.314453125,\n 37.96152331396614\n ],\n [\n -107.5341796875,\n 36.94989178681327\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Mount Rushmore National Memorial (hereafter referred to as “the memorial”), in western South Dakota, is maintained by the National Park Service (NPS) and includes 1,278 acres of land in the east-central part of the Black Hills. An ongoing challenge for NPS managers at the memorial is providing water from sustainable and reliable sources for operations, staff, and the increasing number of visitors. In 2020, the U.S. Geological Survey (USGS) and NPS completed a hydrological study of the Burnpit well (well 5), a 580-foot-deep open hole groundwater well completed in metamorphic (crystalline) rock at the memorial. The purpose of this study was to estimate the geological and hydraulic properties of the aquifer supplying the well and to determine the water quality of the groundwater from the well. The study provides NPS staff and managers background information for assessing future uses for the well. Methods for data collection and analysis for the study included borehole and video camera analysis in 2020, aquifer testing by the NPS in 2009 and the USGS in 2020, and water-quality sampling in 2020.
Borehole camera video generally matched the lithology recorded in the well log. Fractures recorded in the well log and observed with the borehole camera, including more than 20 less prominent fractures and rough sidewall areas, indicated a fractured aquifer. The fractures are the primary conduits for groundwater flow through the rock and into the well.
Transmissivity was estimated for the upper and lower water-level drawdown zones at the Burnpit well with data from the NPS and USGS using the Theis and Cooper-Jacob methods. Transmissivity for the NPS test using the Theis method was 9.0 and 11 feet squared per day (ft2/d) for the upper and lower drawdown zones, respectively. Using the Cooper-Jacob method, the transmissivity was 22 and 14 ft2/d for the upper and lower drawdown zones of the aquifer, respectively. Transmissivity estimates from data from the USGS test were similar. The Theis method, applied to the upper and lower drawdown zones of the aquifer, produced transmissivity estimates of 7.7 and 10 ft2/d, and the Cooper-Jacob method produced estimates of 9.7 and 12 ft2/d, respectively.
Storativity (specific yield) estimated using the Theis method for the NPS aquifer-test data was 0.85 and 0.92 for the upper and lower drawdown zones of the aquifer, respectively. The Cooper-Jacob method applied to the NPS aquifer-test data produced storativity estimates of 0.11 and 0.50 for the upper and lower drawdown zones, respectively. The Theis method applied to the USGS aquifer-test data estimated storativity values of 0.77 and 1.0 for the upper and lower drawdown zones, respectively. The Cooper-Jacob method estimated storativity of 0.50 and 0.60 for the upper and lower drawdown zones of the USGS aquifer test, respectively. The estimated storativity values from the NPS and USGS aquifer tests for the upper and lower drawdown zones were higher than expected for limestones and schists.
The hypothetical equilibrium drawdown for the Burnpit well was estimated after the NPS test in 2009 at no more, and possibly less, than 35 gallons per minute. The NPS noted that the sustainable yield likely was overestimated because the water level did not stabilize during the NPS aquifer test. The specific capacity for the NPS aquifer test in 2009 was 0.16 gallon per minute per foot ([gal/min]/ft) of drawdown at 3 hours, and the specific capacity for the USGS aquifer test in 2020 was 0.13 (gal/min)/ft of drawdown at 3 hours. The rate of water-level recovery after pumping ceased was 0.017 and 0.013 (gal/min)/ft for the NPS and USGS aquifer tests, respectively. The water-level recovery rate was nearly an order of magnitude less than the specific capacity estimated during pumping, indicating that water levels in the Burnpit well may not recover quickly enough during pumping to provide for a continuous source of water.
Water-quality samples were collected at the Burnpit well on June 24 and July 23, 2020, and analyzed for field-measured properties, major ions, metals, nutrients, and perchlorate. Iron, zinc, and lithium concentrations for unfiltered samples in the well were at least three times greater than the mean filtered sample concentrations reported for crystalline aquifers in the Black Hills. Manganese concentrations were less than the mean concentration for crystalline aquifers but exceeded the U.S. Environmental Protection Agency (EPA) secondary drinking-water standards. The iron concentration from the June 24 sample was about 11 times greater than the EPA secondary drinking-water standards and mean concentrations from crystalline aquifers in the Black Hills. Arsenic concentrations in Burnpit well samples collected in 2020 were greater than the EPA primary drinking-water standard and the mean concentration for crystalline aquifers in the Black Hills. Arsenic occurs naturally in the rock of crystalline aquifers, and concentrations from samples in the Black Hills commonly exceed the EPA primary drinking-water standard of 10 micrograms per liter. High concentrations of arsenic, iron, and manganese metals in the Burnpit well make groundwater from the well in its natural state unusable as a drinking-water source, and water treatment would be necessary to reduce the trace element concentrations to less than the EPA primary and secondary drinking-water standards. However, if the memorial has immediate nonpotable water requirements, such as for construction and fire suppression, groundwater from the Burnpit well could provide water without causing additional stress to current (2021) drinking-water sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215059","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Eldridge, W.G., Hoogestraat, G.K., and Rice, S.E., 2021, Borehole analysis, single-well aquifer testing, and water quality for the Burnpit well, Mount Rushmore National Memorial, South Dakota: U.S. Geological Survey Scientific Investigations Report 2021–5059, 29 p., https://doi.org/10.3133/sir20215059.","productDescription":"Report: vii, 29 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-126498","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386673,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98OZQN9","text":"USGS data release","description":"USGS data release","linkHelpText":"Borehole video and aquifer test data for the Burnpit well, Mount Rushmore National Memorial, South Dakota, 2020"},{"id":386672,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5059/sir20215059.pdf","text":"Report","size":"2.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5059"},{"id":386674,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":386671,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5059/coverthb.jpg"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.0625,\n 43.40903821777055\n ],\n [\n -103.2440185546875,\n 43.40903821777055\n ],\n [\n -103.2440185546875,\n 44.52392653654213\n ],\n [\n -104.0625,\n 44.52392653654213\n ],\n [\n -104.0625,\n 43.40903821777055\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
Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
The HANDLEY nuclear test was detonated at about 2,700 feet below the water table on March 26, 1970, in Pahute Mesa, south-central Nevada. Measured tritium concentrations in boreholes ER-20-12 and PM-3 indicate that a shallow tritium plume has migrated more than 1 mile (mi) downgradient from the HANDLEY test within a semi-perched aquifer and deeper tritium plumes have migrated 4.5 miles (mi) within underlying regional aquifers. Boreholes ER-20-12 and PM-3 are in an area of moderate-to-low transmissivity, but observation of tritium moving 4.5 mi within 40 years of the detonation indicates that high-transmissivity intervals exist. However, the location of these permeable pathways is unknown.
This report integrates geologic, hydrologic, and tritium data to infer the location of permeable pathways near and downgradient from the HANDLEY test. Numerical groundwater-flow and tritium-transport models were developed to estimate hydraulic and transport properties between the HANDLEY test and boreholes ER-20-12 and PM-3. Recharge, hydraulic-conductivity, specific-yield, specific-storage, and effective-porosity distributions were estimated with the numerical models by fitting simulated water-level altitudes, vertical-head differences, aquifer-test transmissivities, tritium concentrations, and drawdowns in wells PM-3-1 and PM-3-2 to measured equivalents. Drawdowns were estimated in wells PM-3-1 and PM-3-2 in response to groundwater withdrawals during the drilling of borehole ER-20-12. A modified hydrostratigraphic framework model (mHFM) was developed that incorporates hydrostratigraphic units (HSUs) from the Pahute Mesa–Oasis Valley hydrostratigraphic framework model (PMOV HFM). HSUs in the mHFM were modified from the PMOV HFM by grouping HSUs that, conceptually, are hydraulically similar and splitting HSUs based on water-level, aquifer-test, and tritium data.
Shallow and deeper tritium plumes have migrated to borehole ER-20-12 from the HANDLEY test. The shallow plume migrated from the HANDLEY test through the Timber Mountain welded tuff aquifer, whereas the deeper plumes moved through the Belted Range aquifer (BRA) and modified pre-Belted Range lava flow aquifer (mPBRLFA). Simulated tritium concentrations indicate that the leading edges of tritium plumes reached borehole ER-20-12 by 1990. From 1970 to 2020, the simulated tritium load mostly occurs between borehole ER-20-12 and the HANDLEY test.
An unmapped permeable feature was simulated between borehole ER-20-12 and the downgradient Ribbon Cliff structural zone. This permeable feature hydraulically connects the BRA and mPBRLFA with the Tiva Canyon aquifer (TCA). The TCA is the most transmissive unit in the study area. Simulated tritium from the deeper plumes moves through the permeable feature downgradient from borehole ER-20-12 and then migrates toward well PM-3-1 through the TCA. The leading edge of the deeper simulated tritium plumes reaches well PM-3-1 by 2010.
The mHFM and PMOV HFM do not include a permeable HSU at the water table near borehole PM-3, which is necessary for numerical flow and transport models to match measured water levels, transmissivities, and tritium concentrations in well PM-3-2. Consistently higher measured tritium concentrations in shallow well PM-3-2, compared to deeper well PM-3-1, and a downward vertical gradient between these wells indicate that a permeable feature exists near the water table that causes faster tritium migration toward the shallow well. Reevaluation of the PMOV HFM and geologic investigations, such as drilling another well, are needed to more precisely understand the shallow permeable pathway from the Handley test to well PM-3-2.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215032","collaboration":"Prepared in cooperation with the U.S. Department of Energy, National Nuclear Security Administration Nevada Site Office, Office of Environmental Management, under Interagency Agreement DE-EM0004969","usgsCitation":"Jackson, T.R., 2021, Permeable groundwater pathways and tritium migration patterns from the HANDLEY underground nuclear test, Pahute Mesa, Nevada: U.S. Geological Survey Scientific Investigations Report 2021–5032, 49 p., https://doi.org/10.3133/sir20215032.","productDescription":"Report: vii, 49 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-120498","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":386552,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5032/coverthb.jpg"},{"id":386553,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5032/sir20215032.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5032"},{"id":386554,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YRDQSN","text":"USGS data release","description":"USGS data release.","linkHelpText":"MODFLOW-2005 and MT3DMS models and supplemental data used to simulate groundwater flow and tritium transport from the HANDLEY underground nuclear test, Pahute Mesa, southern Nevada"}],"country":"United States","state":"Nevada","otherGeospatial":"Pahute Mesa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.333984375,\n 36.491973470593685\n ],\n [\n -115.79589843749999,\n 36.491973470593685\n ],\n [\n -115.79589843749999,\n 37.94419750075404\n ],\n [\n -117.333984375,\n 37.94419750075404\n ],\n [\n -117.333984375,\n 36.491973470593685\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
Landscape change in Tuolumne Meadows, Yosemite National Park, California, was characterized using data derived from four lidar surveys: one airborne survey in 2006 and three terrestrial surveys in 2016, 2017, and 2018. These surveys were used to generate a better quantitative understanding of changes associated with fluvial processes along the reach of the Tuolumne River within Tuolumne Meadows. This research was performed to provide a scientific basis for restoration and management decisions made by the National Park Service in accordance with the Tuolumne Wild and Scenic River Final Comprehensive Management Plan. A total of 15 reaches of the streambanks along the Tuolumne River in Tuolumne Meadows were subject to measurable streambank erosion between 2006 and 2018. In these areas, streambank retreat rates ranged between 0 and 2.7 meters per year (m/yr), recorded as an average retreat distance along the length of changing streambank position, with most retreat rates being less than 0.50 m/yr. The highest streambank retreat rates are associated with a year of high spring streamflow in 2017. Based on the data available, it was concluded that deposition on channel and point bars balances streambank erosion over a period of 12 years along the Tuolumne River in Tuolumne Meadows. As such, the river could be considered to be in a state of dynamic equilibrium during this period; erosion and sedimentation occur in distinct pulses in response to hydrological forcing but it is not clear that there is a trend towards sediment accumulation or removal in Tuolumne Meadows nor is there an obvious trend toward channel widening or narrowing. The existence of visible paleochannels in the meadow are an indication that more dramatic channel planform geometry changes have occurred in Tuolumne Meadows over an undetermined period and may occur again in the future. Geomorphic change rates relate to hydrology; during the study period, the high water in 2017 led to the highest rates of geomorphic change. Land managers should anticipate that floods with discharge rates greater than the peak flow in 2017 may cause more substantial landscape change than what was observed in this study, but erosion resulting from these events may be balanced by channel and point-bar deposition over a period of years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215025","collaboration":"Prepared in cooperation with National Park Service","usgsCitation":"DeLong, S.B., Pickering, A.J., and Kuhn, T., 2021, Streambank erosion and related geomorphic change in Tuolumne Meadows, Yosemite National Park, California: U.S. Geological Survey Scientific Investigations Report 2021–5025, 87 p., https://doi.org/10.3133/sir20215025.","productDescription":"viii, 87 p.","numberOfPages":"87","onlineOnly":"Y","ipdsId":"IP-118934","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":386473,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5025/sir20215025.pdf","text":"Report","size":"45 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":386472,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5025/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.03936767578124,\n 37.461778479617465\n ],\n [\n -118.85284423828124,\n 37.461778479617465\n ],\n [\n -118.85284423828124,\n 38.0091482264894\n ],\n [\n -120.03936767578124,\n 38.0091482264894\n ],\n [\n -120.03936767578124,\n 37.461778479617465\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Earthquake Science Center—Menlo Park, Calif. Office
U.S. Geological Survey
345 Middlefield Road, MS 977
Menlo Park, CA 94025
A three-dimensional hydrogeologic framework of the Hot Springs anticlinorium beneath Hot Springs National Park, Arkansas, was constructed to represent the complex hydrogeology of the park and surrounding areas to depths exceeding 9,000 feet below ground surface. The framework, composed of 6 rock formations and 1 vertical fault emplaced beneath the thermal springs, was discretized into 19 layers, 429 rows, and 576 columns and incorporated into a 3-dimensional steady-state groundwater-flow model constructed in MODFLOW-2005. Historical daily mean thermal spring flows were simulated for one stress period of approximately 34 years (1980–2014), chosen to represent the period of record for historical climate data used in the quantification of the boundary conditions. The groundwater-flow model was manually calibrated to historical daily mean thermal spring flows of 88,000 cubic feet per day observed over a 12-year period of record (1990–1995 and 1998–2005) at the thermal springs collection system. Calibration was achieved by calculating starting heads and general head boundary conditions from the Bernoulli equation and then adjusting the horizontal and vertical hydraulic conductivities of the rock formations and vertical fault and the hydraulic conductance of head-dependent flux boundaries. The groundwater-flow model was coupled to a surface-water model developed in the Precipitation-Runoff Modeling System (PRMS) by using PRMS-simulated gravity drainage as a specified flux recharge boundary condition in the groundwater-flow model. Together, the coupled models were used to (1) locate the areas of groundwater recharge to the thermal springs in the discretized hydrogeologic framework by using forward and reverse particle-tracking capabilities of MODPATH, (2) simulate the effects of variable recharge rates on the spring flows at the thermal springs, and (3) assess possible effects of climate and land-use change on the long-term variability of spring flows at the thermal springs.
Forward and backward particle-tracking maps indicated that the most prevalent areas of recharge in the discretized hydrogeologic framework used in this study were within about 0.6–0.9 mile of the thermal springs. Forward particle tracking indicated a recharge area southwest of the thermal springs that corresponded to a location where the predominant lithologies are the Arkansas Novaculite, Hot Springs Sandstone, and Bigfork Chert. Backward particle tracking indicated a second localized area of recharge to the northeast of the thermal springs that corresponded to a location where the dominant lithology is the Bigfork Chert. The groundwater-flow model indicated that the most probable recharge formations are the Arkansas Novaculite, Bigfork Chert, and Hot Springs Sandstone.
The simulated effects of climate and land-use changes on the variability of the spring-flow rates at the thermal springs generally resulted in reductions of thermal spring flow attributed to urban development and more extreme climates characterized by elevated mean surface air temperatures. The groundwater-flow model predicted a linear relation between the thermal spring discharge and the cumulative recharge volume applied to the hydrogeologic framework, and the positive slope of the predicted relation between recharge and simulated thermal spring flow indicates that more extreme precipitation events that supply more recharge may in fact increase the thermal spring-flow rates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215045","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Hart, R.M., Ikard, S.J., Hays, P.D., and Clark, B.R., 2021, Effects of climate and land-use change on thermal springs recharge—A system-based coupled surface-water and groundwater-flow model for Hot Springs National Park, Arkansas: U.S. Geological Survey Scientific Investigations Report 2021–5045, 38 p., https://doi.org/10.3133/sir20215045.","productDescription":"Report: viii, 38 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-091576","costCenters":[{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true},{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"links":[{"id":386401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5045/coverthb.jpg"},{"id":386402,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5045/sir20215045.pdf","text":"Report","size":"43.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5045"},{"id":386403,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SBJVVL","text":"USGS data release","linkHelpText":"Model inputs and outputs for simulating and predicting the effects of climate and land-use changes on thermal springs recharge—A system-based coupled surface-water and groundwater-flow model for Hot Springs National Park, Arkansas"},{"id":386404,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5045/images"}],"country":"United States","state":"Arkansas","otherGeospatial":"Hot Springs National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.1475830078125,\n 34.487881874939866\n ],\n [\n -92.96012878417969,\n 34.487881874939866\n ],\n [\n -92.96012878417969,\n 34.57273337081573\n ],\n [\n -93.1475830078125,\n 34.57273337081573\n ],\n [\n -93.1475830078125,\n 34.487881874939866\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, TN 37211
The Upper Yampa River Basin drains approximately 2,100 square miles west of the Continental Divide in north-western Colorado. There is a growing need to understand potential changes in the quantity and quality of water resources as the basin is undergoing increasing land and water development to support growing municipal, industrial, and recreational needs. The U.S. Geological Survey, in cooperation with stakeholders in the Upper Yampa River Basin water community, began a study to characterize and identify changes in streamflow and selected water-quality constituents, including suspended sediment, Kjeldahl nitrogen, total nitrogen, total phosphorus, and orthophosphate, in the basin. This study used streamflow and water-quality data from selected U.S. Geological Survey sites to provide a better understanding of how major factors, including land use, climate change, and geological features, may influence streamflow and water quality.
Analysis of long-term (1910–2018) and short-term (1992–2018) records of streamflow at main-stem Yampa River and tributary sites indicate downward trends in one or more streamflow statistics, including 1-day maximum, mean, and 7-day minimum. Long-term downward trends in daily mean streamflow in April (22 percent overall) at Yampa River at Steamboat Springs, Colorado, correspond to observed changes in streamflow documented across western North America and the Colorado River Basin that are predominately associated with changes in snowmelt runoff and temperatures. During the short-term period of analysis, decreases in streamflow at main-stem Yampa River and some tributary sites are likely related to changes in consumptive use and reservoir management or, at sites with no upstream flow impoundments, changes in irrigation diversions and climate.
Concentrations of water-quality constituents were typically highest in spring (March, April, and May) during the early snowmelt runoff period as material that is washed off the land surface drains into streams. Highest concentrations occurred slightly later, in May, June, and July, at Yampa River above Stagecoach Reservoir, Colo., and slightly earlier, in February and March at Yampa River at Milner, Colo., indicating that these sites may have different or additional sources of phosphorus from upstream inputs. Yampa River at Milner, Colo., and Yampa River above Elkhead Creek, Colo., had the highest net yields of suspended sediment, Kjeldahl nitrogen, and total phosphorus, and are likely influenced by land use and erosion as the basins of both of these sites are underlain by highly erodible Cretaceous shales.
Upward trends in estimated Kjeldahl nitrogen and total phosphorus concentrations and loads were found at Yampa River at Steamboat Springs, Colo. From 1999 to 2018, the Kjeldahl nitrogen concentration increased by 10 percent or 0.035 milligram per liter, and load increased by 22 percent or 26 tons. Total phosphorus concentration increased by 20 percent or 0.0081 milligram per liter, and loads increased by 41 percent or 6.2 tons. Decreases in streamflow and changes in land use may contribute to these trends.
During multiple summer sampling events at Stagecoach Reservoir, the physical and chemical factors indicated conditions conducive to cyanobacterial blooms, including surface-water temperatures greater than 20 degrees Celsius and total phosphorus and total nitrogen concentrations in exceedance of Colorado Department of Public Health and Environment interim concentrations for water-quality standards. Local geological features (predominately sandstones and shales) and additional inputs from upstream land use likely contribute to the elevated nutrient conditions in Stagecoach Reservoir.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215016","isbn":"978-1-4113-4402-0","collaboration":"Prepared in cooperation with Upper Yampa River Watershed Group, Upper Yampa Water Conservancy District, Colorado Water Conservation Board, Yampa-White-Green Basin Roundtable, Mount Werner Water and Sanitation District, Routt County, Colorado, and the city of Steamboat Springs, Colorado","usgsCitation":"Day, N.K., 2021, Assessment of streamflow and water quality in the Upper Yampa River Basin, Colorado, 1992–2018: U.S. Geological Survey Scientific Investigations Report 2021–5016, 45 p., https://doi.org/10.3133/sir20215016.","productDescription":"Report: vii, 45 p.; Data Release","onlineOnly":"N","ipdsId":"IP-118673","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":386393,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5016/coverthb.jpg"},{"id":386394,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5016/sir20215016.pdf","text":"Report","size":"4.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5016"},{"id":386395,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L7S3NQ","text":"USGS data release","linkHelpText":"Input and output data from streamflow and water-quality regression models used to characterize streamflow and water-quality conditions in the Upper Yampa River Basin, Colorado, from 1992-2018"}],"country":"United States","state":"Colorado","otherGeospatial":"Upper Yampa River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.314453125,\n 40.052847601823984\n ],\n [\n -106.424560546875,\n 40.052847601823984\n ],\n [\n -106.424560546875,\n 40.9964840143779\n ],\n [\n -107.314453125,\n 40.9964840143779\n ],\n [\n -107.314453125,\n 40.052847601823984\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
Annual exceedance probability flows at gaged locations and regional regression equations used to estimate annual exceedance probability flows at ungaged locations were developed by the U.S. Geological Survey, in cooperation with the Mississippi Department of Transportation, to improve flood-frequency estimates at rural streams in the alluvial plain of the lower Mississippi River. These estimates were developed using current geospatial data, analytical methods, and annual peak-flow data through September 2017 at 58 streamgages in the alluvial plain of the lower Mississippi River, including 9 in Mississippi, 35 in Arkansas, 4 in Missouri, and 10 in Louisiana. Annual exceedance probability flows presented in this report incorporate streamflow data through the 2017 water year, 32 additional years of record since the previous study in 1985 of flood magnitude and frequency in the Mississippi portion of the alluvial plain of the lower Mississippi River. Ranges for standard error of prediction, average variance of prediction, and pseudo-R2 are 45–61 percent, 0.035–0.059 (log cubic feet per second)2, and 90–94 percent, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215046","collaboration":"Prepared in cooperation with the Mississippi Department of Transportation","usgsCitation":"Anderson, B.T., 2021, Magnitude and frequency of floods in the alluvial plain of the lower Mississippi River, 2017: U.S. Geological Survey Scientific Investigations Report 2021–5046, 15 p., https://doi.org/10.3133/sir20215046.","productDescription":"iv, 15 p.","numberOfPages":"24","onlineOnly":"N","ipdsId":"IP-118369","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":386320,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5046/images"},{"id":386316,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5046/coverthb.jpg"},{"id":386317,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5046/sir20215046.pdf","text":"Report","size":"2.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5046"}],"country":"United States","state":"Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, Tennessee","otherGeospatial":"Alluvial plain of the lower Mississippi River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.36279296875,\n 37.16031654673677\n ],\n [\n -90.3076171875,\n 36.914764288955936\n ],\n [\n -90.791015625,\n 36.19109202182454\n ],\n [\n -91.51611328125,\n 34.867904962568716\n ],\n [\n -92.0654296875,\n 33.99802726234877\n ],\n [\n -92.21923828124999,\n 32.713355353177555\n ],\n [\n -92.83447265624999,\n 30.845647420182598\n ],\n [\n -92.7685546875,\n 29.516110386062277\n ],\n [\n -91.97753906249999,\n 29.11377539511439\n ],\n [\n -90.4833984375,\n 28.844673680771795\n ],\n [\n -89.12109375,\n 28.94086176940557\n ],\n [\n -89.23095703125,\n 29.554345125748267\n ],\n [\n -89.49462890625,\n 30.35391637229704\n ],\n [\n -89.9560546875,\n 30.713503990354965\n ],\n [\n -90.263671875,\n 31.59725256170666\n ],\n [\n -89.75830078125,\n 34.615126683462194\n ],\n [\n -89.07714843749999,\n 35.639441068973944\n ],\n [\n -88.61572265625,\n 36.474306755095235\n ],\n [\n -88.70361328125,\n 36.914764288955936\n ],\n [\n -89.36279296875,\n 37.16031654673677\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
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This final report summarizes activities, outcomes, and lessons learned from a 3-year project titled “Climate Change Adaptation for Coastal National Wildlife Refuges” with the Cape Romain National Wildlife Refuge (NWR) and local partners in the surrounding South Carolina Lowcountry. The Lowcountry is classified as the 10-county area encompassing the coastal plain of South Carolina (this report specifically focuses on Berkeley, Charleston, and Georgetown Counties). The goals of this work, sponsored by the U.S. Geological Survey’s Southeast Climate Adaptation Science Center (SECASC), were to foster active engagement with stakeholders; to develop a comprehensive definition of adaptation problems faced by agencies, organizations, and individuals near the Cape Romain NWR that accounts for global change, local values, knowledge and perceptions; and to encourage social learning and building of effective networks and trust across South Carolina Lowcountry organizations and individuals. Although project scoping began at the scale of the Atlantic seaboard, by engaging with NWRs from Massachusetts to Florida, participating refuge personnel eventually selected the Cape Romain NWR to serve as a case study for testing our goals. The Cape Romain Partnership for Coastal Conservation was established to address global change impacts at a regional level and includes representation from Federal and State resource agencies, local conservation nongovernmental organizations, and organizations representing underserved community interests. Research topics, originating from discussions with Cape Romain Partnership for Coastal Conservation members, focused on quantifying key drivers of change including localized sea-level rise (SLR) predictions, estimates of coastal hurricane inundation as amplified by SLR, and urban growth trends and forecasts. These key drivers provided a foundation to engage stakeholders in planning exercises to begin a process of collective understanding and collaborative decision making. The goal of this process was to develop collective strategies of adaptation to enhance community and ecosystem resilience in the South Carolina Lowcountry.
South Carolina’s Lowcountry is experiencing rapid environmental and social transformation because of SLR rates approaching twice the global average, chronic tidal flooding and catastrophic storm surges, erosion and loss of habitats that provide essential services to wildlife and humans, and increasing social polarization fueled by aggressive low-density urban growth and other forms of land conversion. To support characterizations of plausible future scenarios, we used available or, in some cases, developed new models to project future conditions of key environmental and social-economic drivers. Because of the imprecision of mean global SLR projections, the SECASC commissioned a climatological study to account for local conditions and multiple representative concentration pathways to project a tailored distribution of future sea levels. These projections were matched to SLR scenarios provided by existing models to anticipate the range of future coastal habitat changes in the South Carolina Lowcountry. SLR scenarios were also incorporated into existing storm-surge models, which do not account for alternate baseline sea levels, to project the local effects of future hurricanes. To evaluate the extent and effects of population growth and urban expansion, we relied on an existing urban-growth model to map the spatial distribution of land-conversion probabilities, the total area of which is predicted to increase twofold to threefold over the next 60 years. In addition to this simplified model, an econometric model is in development to account for nonlinear feedback dynamics in land value, land use, and ecosystem service production. Although not yet completed, the goals of this model are to produce more-detailed projections of growth dynamics and to allow predictions of development patterns resulting from alternate land-use planning policies and incentives.
Collaborative planning for an uncertain future requires more than providing decision makers with information on future physical and ecological conditions; developing effective and consensual strategies must also integrate sociological values, multiple cultural perspectives, and an understanding of human behavior. To support broad stakeholder engagement in integrative approaches to adaptation planning, emphasis was placed on the importance of considering differences in how individuals perceive their environment and create meaning. Because cultural frameworks form the basis for perceptions and, ultimately, the behaviors of individuals and institutions, we describe a model of human behavior and how it can be used to understand the effect of cultural complexity and variation in perception on choices, behavioral change, and long-term maintenance of behaviors. We consider a model commonly used in the field of behavioral health that accommodates variation in human perception when describing stages of behavior and the dynamics of behavioral change. Tailoring communication and engagement activities to targeted stakeholders is likely to benefit from increased understanding of behavioral change processes.
The complex nature of this problem limited the usefulness of a traditional decision-analytic approach, we explored alternative methods for engagement, collaborative learning and decision making. Recognizing that project partners and Lowcountry stakeholders may be at different stages of preparedness and interest level for modifying behavior as a function of global change, we facilitated a scenario-planning exercise to familiarize partners with this well-established approach for communicating the opportunities and threats arising under alternative, plausible futures. We developed narratives for four alternative South Carolina Lowcountry scenarios to be used in later strategic planning that focus on quantitative trends for three primary drivers with high impact and high uncertainty: manifestations of climate change, social-political shifts at a global level, and forces of local value and power structures. This scenario-planning exercise underscored the complex relation between the temporospatial scale of the production of ecological goods and services and the institutional scale at which they are managed. We then guided the partners through an assessment of the relevant strengths and weaknesses of the Cape Romain Partnership for Coastal Protection, using the threats and opportunities characterized by each scenario to understand how the partnership might respond when attempting to meet conservation and societal objectives. The partnership identified key strengths including partnership experience, outreach and technical capacities, a substantial conservation land base, and high social cohesion in the South Carolina Lowcountry. Limited communication expertise, institutional inertia, and insufficient staffing and funding were recognized as important weaknesses across the partnership. By examining and scoring combinations of internal strengths and weaknesses and external threats and opportunities, the partnership developed sets of prioritized strategies to consider in the context of a given scenario. Although we had insufficient time to examine all scenarios in detail, the intent was to identify a portfolio of strategic actions to address threats and opportunities represented in multiple plausible futures. Top-ranking strategies encompassed a range of actions that focused on strengthening the conservation community and communicating the benefits of nature (that is, ecosystem services) to leveraging partnerships to expand land protection.
This report also details the methods and preliminary results of several models developed or applied in support of this project. Two parcel-selection algorithms were used to evaluate anticipated habitat changes and patterns of urban growth to guide decisions on optimal conservation reserve design to protect habitat communities. One approach used a widely available planning software (MARXAN) to maximize conservation benefits near the Cape Romain NWR, whereas the other approach was a novel application of economic theory to account for uncertainty in future conditions and for the risks of unanticipated habitat loss. This latter model applies modern portfolio theory to estimate the risk of investing in any portfolio of land parcels (that is, candidate “reserves”) under climate-change uncertainty by quantifying the variation and spatial correlation of conservation benefits derived from each portfolio. We expanded the range of actions beyond simply whether or not to invest in a set of land parcels, an approach commonly used in spatial conservation planning, to also include consideration of divestment from currently protected lands. Such refinements allow for better accounting of system dynamics and can evaluate the benefits of flexible conservation tools such as rolling easements. Model results were conditional on a decision maker’s risk tolerance but highlighted general strategies of land conservation to increase future habitat representation beyond what is expected under the current protected land base. We built models that may help inform coastal planning by estimating salinity dynamics and the performance of oyster reef restoration efforts to predict the combined effects of global change and management of freshwater flows on coastal habitats and the processes that contribute to their resilience. These models can support restoration decisions by evaluating the expected benefits of site locations for shoreline protection and fisheries production. Lastly, we developed a spatially explicit economic model that predicts feedback dynamics among land value, land-use change, and effects on ecosystem service provision to explore zoning policies and incentives on urban growth and ecosystem services.
We summarize these efforts with insights and considerations for the Cape Romain Partnership for Coastal Protection to continue to engage stakeholders in effective adaptation planning. First, notions of place attachment (referred to as sense of place), and the role of culture in social discourse are increasingly being used to understand the complex interactions between society and the environment and how societies respond and adapt to climate change. Sense of place was a unifying theme whenever the future of the South Carolina Lowcountry was discussed. The contribution of the South Carolina Lowcountry’s environmental wealth, rich cultural heritage, and quality of life to sense of place has important implications for how adaptation planning might best be pursued. More community-based governance of the commons (in other words, natural and cultural resources held in common), in which broad stakeholder participation and power sharing are key elements, is considered important. This devolution of governance is characterized by polycentric institutions and self-organizing social networks that promote a local culture of knowledge sharing, problem solving, and learning. These so-called bridging organizations (or individuals) often provide the leadership necessary to bring together potentially disparate Government agencies and institutions, private organizations, and individuals in a collective process of problem solving. Our observations also suggest that the conservation community in the South Carolina Lowcountry views its activities as integral to the broader governance of social-ecological systems, in which responses to the forces of global change are mediated through culture, economics, and politics. Rather than directly competing with other interests, the South Carolina Lowcountry conservation community seems to embrace an interpretation of conservation in which the fundamental objective is the quality of human life rather than environmental protection.
Fundamental to the types of governance reforms described above is the notion of coproduction, in which experts and users collaborate to develop a shared body of knowledge. In this approach, scientists work with stakeholders to help frame questions, design research, and collect and analyze data. Such sustained collaborations are increasingly believed to be an effective way to produce useable (or actionable) science. The emphasis on social learning, leveraging strong social networks, coordinating and deliberating among diverse stakeholders, and applying principles of adaptive management is an essential contribution to adaptive capacity. The diverse and robust set of scientific approaches, methods to help stakeholders collaborate in effective and goal-driven planning processes, and decision tools resulting from this project hopefully will assist Cape Romain NWR and its partners prepare for climatic, ecological, and social changes over the coming decades.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211021","usgsCitation":"Eaton, M.J., Johnson, F.A., Mikels-Carrasco, J., Case, D.J., Martin, J., Stith, B., Yurek, S., Udell, B., Villegas, L., Taylor, L., Haider, Z., Charkhgard, H., and Kwon, C., 2021, Cape Romain Partnership for Coastal Protection: U.S. Geological Survey Open-File Report 2021–1021, 158 p., https://doi.org/10.3133/ofr20211021.","productDescription":"xii, 158 p.","numberOfPages":"174","onlineOnly":"Y","ipdsId":"IP-100705","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":386276,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1021/coverthb.jpg"},{"id":386277,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1021/ofr20211021.pdf","text":"Report","size":"33.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1021"}],"country":"United States","state":"South Carolina","otherGeospatial":"Cape Romain 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 -79.8431396484375,\n 32.78842902722552\n ],\n [\n -79.815673828125,\n 32.765336175015776\n ],\n [\n -79.63577270507811,\n 32.85421076375021\n ],\n [\n -79.55886840820312,\n 32.92455477363828\n ],\n [\n -79.47784423828125,\n 33.00981511270531\n ],\n [\n -79.3487548828125,\n 33.0063602132054\n ],\n [\n -79.27047729492188,\n 33.12490094278685\n ],\n [\n -79.34600830078125,\n 33.16169660598766\n ],\n [\n -79.50393676757812,\n 33.060471419708115\n ],\n [\n -79.60968017578125,\n 32.99599470276581\n ],\n [\n -79.6673583984375,\n 32.93838636388491\n ],\n [\n -79.68658447265625,\n 32.91533251206152\n ],\n [\n -79.8431396484375,\n 32.78842902722552\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Southeast Climate Adaptation Science Center
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Managing nutrient and sediment runoff from fields that drain to the Great Lakes is key to mitigating harmful algal blooms. Implementation of best management practices on agricultural land is considered a critical step to improving water quality in these streams, however the effect of these best management practices is difficult to quantify. The purpose of this study was to use a suite of high-resolution imagery acquired with unmanned aircraft systems (including a combination of visible, multispectral, and thermal cameras) to better characterize edge-of-field (EOF) sites in Michigan and Wisconsin that are monitored in cooperation with the Great Lakes Restoration Initiative. This high-resolution imagery (2.5–12-centimeter ground resolution) was used to delineate artificial subsurface drainage (tile-drain) networks and surface water flow paths that indicate contributing areas (that is, all area that drains to a monitored point) at these EOF sites, providing better characterization of each study site. Contributing areas for these sites ranged from 2.86 to 5.07 hectares and, among the sites, tile drains were identified as those that followed soil properties and those that were more densely patterned networks. These surveys also indicated that the contributing area monitored at the EOF sites may cross field boundaries and is not always coincident with the area underlain by subsurface drainage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215013","usgsCitation":"Webber, J.J., and Williamson, T.N., 2021, Workflow for using unmanned aircraft systems and traditional geospatial data to delineate agricultural drainage tiles at edge-of-field sites: U.S. Geological Survey Scientific Investigations Report 2021–5013, 18 p., https://doi.org/10.3133/sir20215013.","productDescription":"Report: vii, 18 p.; Data Releases: 4","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-118324","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":386180,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5013/images"},{"id":386154,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5013/coverthb.jpg"},{"id":386155,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5013/sir20215013.pdf","text":"Report","size":"17.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5013"},{"id":386156,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EXXX2O","text":"USGS data release","description":"USGS data release","linkHelpText":"Low-altitude visible, multispectral, and thermal-infrared imagery from edge-of-field monitoring sites for Great Lakes Restoration Initiative - 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Streambank erosion and reservoir sedimentation are primary concerns of resource managers in Kansas and throughout many regions of the United States and negatively affect flood control, water supply, and recreation. The Cottonwood and upper Neosho Rivers drain into John Redmond Reservoir, and since reservoir completion in 1964, there has been substantial conservation-pool sedimentation and storage loss in John Redmond Reservoir, causing storage capacity losses more rapidly than most other Federal reservoirs in Kansas. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office, has monitored water quality (temperature, specific conductance, and turbidity) on the Cottonwood River (upstream from the reservoir) and Neosho River (upstream and downstream from the reservoir) since 2007 with additional sites added in 2009. The purpose of this report is to quantify suspended-sediment concentrations, loads, and yields entering and exiting John Redmond Reservoir during January 1, 2010, through December 31, 2019.
Three water-quality monitoring sites were upstream from the reservoir (Cottonwood River near Plymouth, Kansas [USGS site 07182250; hereinafter referred to as “Cottonwood”]; Neosho River at Burlingame Road near Emporia, Kans. [USGS site 07179750; hereinafter referred to as “Burlingame”]; and Neosho River at Neosho Rapids, Kans. [USGS site 07182390; hereinafter referred to as “Neosho Rapids”]), and one water-quality monitoring site was downstream from the reservoir (Neosho River at Burlington, Kans. [USGS site 07182510; hereinafter referred to as “Burlington”]). The Neosho Rapids streamgage is downstream from the confluence of the Cottonwood and upper Neosho Rivers and has a contributing drainage area accounting for 91 percent of the total contributing drainage area to John Redmond Reservoir.
Continuously measured streamflow, water quality, and discrete water-quality data were used to develop updated regression models to compute suspended-sediment concentrations, loads, and yields upstream and downstream from John Redmond Reservoir in east-central Kansas. Several turbidity sensors were deployed during the analysis period, and there are no established relations between the sensors; therefore, individual models for each sensor were developed. Model statistics for the turbidity and suspended-sediment concentration linear regression models were better (based on the coefficient of determination, root mean square error, and model standard percentage error) than the streamflow and suspended-sediment concentration linear regression models, indicating better model performance. Computed concentrations, loads, and yields do not account for the ungaged 9 percent of the drainage basin downstream from the Neosho Rapids streamgage.
Mean daily suspended-sediment loads upstream from the reservoir were largest at Neosho Rapids (2,250 tons), second largest at Cottonwood (2,180 tons), and smallest at Burlingame (624 tons). Streamflow at Burlington was predominately regulated by reservoir releases, and mean daily suspended-sediment loads were smaller (286 tons) than at upstream sites. Among the upstream sites, Cottonwood had the largest mean daily suspended-sediment concentration (179 milligrams per liter [mg/L]), followed by Neosho Rapids (162 mg/L), and Burlingame (108 mg/L). Burlington had the smallest mean daily suspended-sediment concentration of all sites (46 mg/L).
Annual reservoir trapping efficiency ranged from 82 to 94 percent, and the largest sediment mass trapped was during 2019 (2,230,000 tons). Reservoir storage decreased an estimated 7,750 acre-feet during 2010 and 2014–19. Using the mean trapping efficiency to estimate suspended-sediment loads during years with missing data (2011–13), the total estimated reservoir storage lost to sedimentation for the analysis period (2010–19) was 8,690 acre-feet, about 17 percent of the remaining storage space reported in 2007. The mean annual sedimentation rate during the analysis period (747 acre-feet per year) was about 85 percent larger than the design sedimentation rate (404 acre-feet per year) originally projected during construction. Different reservoir outflow management strategies, including operating near normal capacity as opposed to higher flood pool levels, could reduce the total reservoir storage lost by 3 percent (about 261 acre-feet), which is equal to 14 percent of the total sediment removed during the dredging operation in 2016.
During the study period, about 56 percent of the total suspended-sediment load was transported during streamflows greater than the National Weather Service flood action stage at the upstream sites (0.1–5 percent of the record; Cottonwood mean: 48 percent; Burlingame mean: 40 percent; Neosho Rapids mean: 78 percent). Disproportionately large sediment loads were delivered during short periods of time, and localized efforts of stream erosion protection (streambank stabilization, riparian buffers) were likely to be overwhelmed. Precipitation frequency and intensity are projected to continue to increase in this region; therefore, future sediment reduction strategies that account for extreme episodic events may be beneficial. Changes to reservoir outflow management could also minimize sediment accumulation while still preserving flood control. Continued investigation of sediment reduction measures is necessary for future mitigation with the understanding that sedimentation rate is largely driven by high flows. Results from this study can be used to calibrate sediment models, explore sediment reduction strategies, highlight the importance of continued water-quality monitoring to determine effectiveness and changes in sediment transport, and assess the ability of John Redmond Reservoir to support designated uses into the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215037","collaboration":"Prepared in cooperation with the Kansas Water Office","usgsCitation":"Kramer, A.R., Peterman-Phipps, C.L., Mahoney, M.D., and Lukasz, B.S., 2021, Sediment concentrations and loads upstream from and through John Redmond Reservoir, east-central Kansas, 2010–19: U.S. Geological Survey Scientific Investigations Report 2021–5037, 49 p., https://doi.org/10.3133/sir20215037.","productDescription":"Report: ix, 50 p; Appendixes: 12; Dataset","numberOfPages":"64","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119997","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":386084,"rank":11,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5037/sir20215037_appendix09.pdf","text":"Appendix 9","size":"457 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5037 Appendix 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1217 Biltmore Drive
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This report provides the necessary documentation of the numerical models developed for the Central Sands Lake study in central Wisconsin and will be included as a technical appendix in the report to the Wisconsin State Legislature by the Wisconsin Department of Natural Resources (WDNR) in response to 2017 Wisconsin Act 10. This legislation directed WDNR to determine whether existing and potential groundwater withdrawals are causing or are likely to cause significant reduction of mean seasonal water levels at Pleasant Lake, Long Lake, and Plainfield Lake (s. 281.34(7m)(2)(b), Wis. Stats.) in Waushara County, Wisconsin. To evaluate the potential hydrologic connection between groundwater withdrawals and the nearby study lakes, hydrologic models were created that focused on the lakes of interest and yet were large enough to cover a broad enough region to extend to the major hydrologic boundaries of the natural flow system. The areas near the lakes require finer-scale grid discretization (or spacing) to better represent the lakes and streams in the model, but also need to cover a large enough area to include the groundwater withdrawal locations that have the potential to cause reduction in water levels in the lakes. To accomplish these goals, three groundwater models were created: a regional model extending to major hydrologic boundaries; and two inset models, inheriting boundaries from the regional model but focused near the lakes. Each of the inset models, in turn, included a detailed area close to the lakes surrounded by an area at the same spatial scale as the regional model (Figure 1).
To support WDNR in evaluating the connection between groundwater withdrawals and lake levels, a representative time period was required over which to compare land use with and without irrigated agriculture and for WDNR to evaluate potential lake stage and flux changes related to irrigated agriculture. WDNR chose the climate period of 1981-2018 to be representative of a typical period and provided two land use scenarios—one with no irrigated agriculture and one with assumed crop rotations similar to current conditions—to simulate with groundwater models to, then, compare lake responses with. As a result, simulations over this climate record are not intended to recreate the history of 1981-2018 because land use changed over that time. These runs are, instead, intended to provide a basis on which to compare land use with and without irrigation-related groundwater withdrawals based on the current arrangement of land use and a varied climatic record. Groundwater withdrawals focused on irrigated-agriculture-related water use because greater than 95% of groundwater withdrawal in the two inset models around the study lakes is for irrigated agriculture water use.
The period of 2012-2018 was used for parameter estimation (synonymously referred to as “history matching”) for the groundwater models. This time period was chosen because it includes the most complete water use records to simulate groundwater withdrawals. History matching was performed using groundwater elevations, lake stages, and streamflow observations over the 2012-2018 time period and processed observations derived from those raw data.
Climatic data were incorporated into the model using a soil-water balance approach. A soil water balance model was constructed at the scale of the regional groundwater model to both calculate recharge based on land use and climate, and in the long-term climate-period runs, to estimate water use required by irrigated agriculture to apply as well boundary conditions in the groundwater model in the absence of reported water use values over that period.
","language":"English","publisher":"Wisconsin Department of Natural Resources","usgsCitation":"Fienen, M., Haserodt, M.J., Leaf, A.T., and Westenbroek, S., 2021, Appendix C: Central sands lakes study technical report: Modeling documentation: Wisconsin DNR Technical Report, ix, 137 p.","productDescription":"ix, 137 p.","ipdsId":"IP-127829","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":386002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385990,"type":{"id":15,"text":"Index Page"},"url":"https://dnr.wisconsin.gov/topic/Wells/HighCap/CSLStudy.html"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Central Sands region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.78851318359375,\n 43.58834891179792\n ],\n [\n -89.29962158203125,\n 43.57641143300888\n ],\n [\n -89.219970703125,\n 43.75919263886012\n ],\n [\n -89.54132080078125,\n 44.471031231561845\n ],\n [\n -89.7967529296875,\n 44.41808794374846\n ],\n [\n -89.85443115234375,\n 44.33367180085156\n ],\n [\n -89.98901367187499,\n 44.11125397357155\n ],\n [\n -90.01373291015625,\n 44.03232064275081\n ],\n [\n -89.96978759765625,\n 43.878097874251736\n ],\n [\n -89.8187255859375,\n 43.71156424665851\n ],\n [\n -89.78851318359375,\n 43.58834891179792\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haserodt, Megan J. 0000-0002-8304-090X mhaserodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8304-090X","contributorId":174791,"corporation":false,"usgs":true,"family":"Haserodt","given":"Megan","email":"mhaserodt@usgs.gov","middleInitial":"J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leaf, Andrew T. 0000-0001-8784-4924 aleaf@usgs.gov","orcid":"https://orcid.org/0000-0001-8784-4924","contributorId":5156,"corporation":false,"usgs":true,"family":"Leaf","given":"Andrew","email":"aleaf@usgs.gov","middleInitial":"T.","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":816549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Westenbroek, Stephen, M. 0000-0002-6284-8643","orcid":"https://orcid.org/0000-0002-6284-8643","contributorId":206429,"corporation":false,"usgs":true,"family":"Westenbroek","given":"Stephen, M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816550,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}