About one-quarter of the water supply for the Village of Ruidoso, New Mexico, is from groundwater pumped from wells located along North Fork Eagle Creek in the National Forest System lands of the Lincoln National Forest near Alto, New Mexico. Because of concerns regarding the effects of groundwater pumping on surface-water hydrology in the North Fork Eagle Creek Basin and the effects of the 2012 Little Bear Fire, which resulted in substantial loss of vegetation in the basin, the U.S. Department of Agriculture Forest Service, Lincoln National Forest, has required monitoring of a portion of North Fork Eagle Creek for short-term geomorphic change as part of the permitting decision that allows for the continued pumping of the production wells. The objective of this study is to address the geomorphic monitoring requirements of the permitting decision by conducting annual geomorphic surveys of North Fork Eagle Creek along the stream reach between the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station (U.S. Geological Survey [USGS] site 08387550) and the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station (USGS site 08387600). The monitoring of short-term geomorphic change in the stream reach began in June 2017 with surveys of select cross sections and surveys of all woody debris accumulations and pools found in the channel. In June 2018, the monitoring of short-term geomorphic change continued with another geomorphic survey of the stream reach (with some modification to the monitoring methods).
The 2017 and 2018 surveys were conducted by the USGS, in cooperation with the Village of Ruidoso, and were the first two in a planned series of five annual geomorphic surveys. The results of the 2017 geomorphic survey were summarized and interpreted in a previous USGS Open-File Report, and the data were published in the companion data release of that report. In this report, the results of the 2018 geomorphic survey are summarized, interpreted, and compared to the results of the 2017 survey. The data from the 2018 geomorphic survey are published in the companion data release of this report.
The study reach surveyed in June 2018 is 1.89 miles long, beginning about 260 feet upstream from the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station and ending at the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station. Large sections of the study reach are characterized by intermittent streamflow, and where streamflow is normally continuous (including at the upper and lower portions of the study reach, near the streamflow-gaging stations), the streamflow typically remains less than 2 cubic feet per second throughout the year except during seasonal high flows, which most often result from rainfall during the North American monsoon months of July, August, and September or from snowmelt runoff in March, April, and May. Between the 2017 and 2018 surveys, high-flow events resulting from both rainfall (during the North American monsoon season) and snowmelt runoff (during the winter) occurred in the study reach, and those high-flow events appeared to have caused some minor and localized geomorphic changes in the study reach, which were evaluated through comparison of the 2017 and 2018 survey results.
For the 2017 geomorphic survey of North Fork Eagle Creek, cross sections were established and surveyed at 14 locations along the study reach, and in 2018, those same 14 cross sections were resurveyed. Comparisons of the cross-section survey results indicated that minor observable geomorphic changes had occurred in 3 of the 14 cross sections. These minor observable geomorphic changes included aggradation or degradation of surface materials by about 1–2 feet in some parts of the affected cross sections.
To further assess geomorphic changes within the study reach, other features, including woody debris accumulations and pools, were surveyed in both 2017 and 2018. During the 2018 geomorphic survey, 112 distinct accumulations of woody debris and 71 pools were identified in the study reach. Charred wood or burn-marked wood was present in at least 17 of the identified woody debris accumulations (and was present in some of the woody debris accumulations identified during the 2017 survey), indicating that some of the woody debris in the channel may have been sourced from trees or forest litter that had burned during 2012 Little Bear Fire. Only 22 of the 112 woody debris accumulations identified during the 2018 survey were certain to have also been present during the 2017 survey (when 58 woody debris accumulations were identified), indicating that most of the woody debris accumulations surveyed in 2017 were likely transported during the high-flow events between the 2017 and 2018 surveys but also indicating that the flows during those events were not high enough to remove some of the more firmly anchored woody debris accumulations. Most woody debris accumulations identified in 2018 did not appear to have substantially influenced geomorphic change in the locations where they were found. However, the formation of 10 of the 71 pools identified in the study reach in 2018 appeared to have been influenced by the presence of woody debris, indicating that some woody debris accumulations may have driven local geomorphic changes. Notably, pool totals from the 2017 survey could not be accurately compared to the pool totals from the 2018 survey because of differences between the two surveys in the methods used to identify pools.
Because the study began 5 years after the 2012 Little Bear Fire, and because the period and geomorphic scope of the study have so far been limited, it cannot be said that the geomorphic changes observed between the 2017 and 2018 surveys are representative of a pattern of geomorphic change following the 2012 Little Bear Fire. Though, once geomorphic changes between the 2017 and 2018 surveys can be compared with results from geomorphic surveys planned for 2019, 2020, and 2021, it may be possible to develop an understanding of the patterns in geomorphic change following the 2012 Little Bear Fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201121","collaboration":"Prepared in cooperation with the Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., 2020, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2018: U.S. Geological Survey Open-File Report 2020–1121, 37 p., https://doi.org/10.3133/ofr20201121.","productDescription":"Report: v, 37 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-112737","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":381235,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1121/ofr20201121.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1121"},{"id":381236,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94ZQHKU","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data supporting the 2018 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":381234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1121/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5621337890625,\n 32.99023555965106\n ],\n [\n -104.7930908203125,\n 32.99023555965106\n ],\n [\n -104.7930908203125,\n 33.770015152780125\n ],\n [\n -105.5621337890625,\n 33.770015152780125\n ],\n [\n -105.5621337890625,\n 32.99023555965106\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Soil and water conservation: A celebration of 75 years","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Soil and Water Conservation Society","usgsCitation":"Mushet, D.M., and Aram Calhoun, 2020, Wetland conservation in the United States: A swinging pendulum, chap. 14 of Soil and water conservation: A celebration of 75 years, p. 162-171.","productDescription":"10 p.","startPage":"162","endPage":"171","ipdsId":"IP-115447","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":381440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381426,"type":{"id":15,"text":"Index Page"},"url":"https://www.swcs.org/resources/publications/soil-and-water-conservation-a-celebration-of-75-years"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":807027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aram Calhoun","contributorId":245784,"corporation":false,"usgs":false,"family":"Aram Calhoun","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":807028,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216899,"text":"ofr20201140 - 2020 - Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2019","interactions":[],"lastModifiedDate":"2020-12-15T19:44:17.549778","indexId":"ofr20201140","displayToPublicDate":"2020-12-15T08:17:17","publicationYear":"2020","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":"2020-1140","displayTitle":"Continuous Stream Discharge, Salinity, and Associated Data Collected in the Lower St. Johns River and Its Tributaries, Florida, 2019","title":"Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2019","docAbstract":"The U.S. Army Corps of Engineers, Jacksonville District, is deepening the St. Johns River channel in Jacksonville, Florida, from 40 to 47 feet along 13 miles of the river channel beginning at the mouth of the river at the Atlantic Ocean, in order to accommodate larger, fully loaded cargo vessels. The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, monitored stage, discharge, and (or) water temperature and salinity at 26 continuous data collection stations in the St. Johns River and its tributaries.
This is the fourth annual report by the U.S. Geological Survey on data collection for the Jacksonville Harbor deepening project. The report contains information pertinent to data collection during the 2019 water year, from October 2018 to September 2019. No changes to the previously installed data collection network were made during this period.
Discharge and salinity varied widely during the data collection period, which included above-average rainfall for all counties in the study area over the 3-month period from November to January, below-average annual rainfall for all counties, and effects from Hurricane Dorian in September 2019. Total annual rainfall for all counties ranked third among the annual totals computed for the 4 years considered for this study. Annual mean discharge at Durbin Creek was highest among the tributaries, followed by Trout River, Ortega River, Julington Creek, Pottsburg Creek, Broward River, Cedar River, Clapboard Creek, and Dunn Creek. The annual mean discharge for each of the main-stem sites was lower for the 2019 water year than for the 2018 water year. Since the beginning of the study in 2016, the St. Johns River at Astor station computed its lowest annual mean discharge, the Jacksonville station recorded its second lowest, and the Buffalo Bluff station recorded its second highest in 2019.
Among the tributary sites, annual mean salinity was highest at Clapboard Creek, the site closest to the Atlantic Ocean, and was lowest at Durbin Creek, the site farthest from the ocean. Annual mean salinity data from the main-stem sites on the St. Johns River indicate that salinity decreased with distance upstream from the ocean, which was expected. Relative to annual mean salinity calculated for the 2018 water year, annual mean salinity at all monitoring locations was higher for the 2019 water year except at the main-stem site below Shands Bridge and at the tributary sites of Durbin Creek and Julington Creek, which remained the same. The 2019 annual mean salinity at Dunn Creek was the highest on record for that site, and Clapboard Creek and Trout River were the second highest on record for those sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201140","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Ryan, P.J., 2020, Continuous stream discharge, salinity, and associated data collected in the lower St. Johns River and its tributaries, Florida, 2019: U.S. Geological Survey Open-File Report 2020–1140, 48 p., https://doi.org/10.3133/ofr20201140.","productDescription":"ix, 48 p.","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-118214","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":381275,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1140/coverthb.jpg"},{"id":381276,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1140/ofr20201140.pdf","text":"Report","size":"7.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1140"}],"country":"United States","state":"Florida","otherGeospatial":"St John's River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.93878173828125,\n 29.1161749329972\n ],\n [\n -81.4691162109375,\n 29.1161749329972\n ],\n [\n -81.4691162109375,\n 30.545704405480997\n ],\n [\n -81.93878173828125,\n 30.545704405480997\n ],\n [\n -81.93878173828125,\n 29.1161749329972\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Biological conservation is a fundamental purpose of the National Park system, and Catoctin Mountain Park (CATO) supports high-quality habitat for native fishes in the headwaters of the Chesapeake Bay watershed in eastern North America. However, native Blue Ridge sculpin (Cottus caeruleomentum) have been extirpated in Big Hunting Creek above Cunningham Falls in CATO. Prior research indicates that infection by the fungal-like protist Dermocystidium is a likely cause for the extirpation, but elevated stream temperatures also have been observed in the study area, and it remains unknown whether thermal stress may exacerbate infections or otherwise limit habitat suitability for fishes in CATO.
The purpose of this study was to quantify spatial variation in summer stream temperatures and to evaluate the effects of temperature on sculpin growth rates and susceptibility to Dermocystidium infection. We used observational and experimental methods to address these objectives. First, we deployed stream temperature gages at 10 sites throughout the study area to assess hourly and daily temperatures during the summer of 2019. Second, we conducted an in situ fish enclosure experiment at five of the temperature sites to assess fish growth and susceptibility to Dermocystidium infection over a 45-day exposure period. For this experiment we collected sculpin from a stream in CATO that supports a robust population of Blue Ridge sculpin (Owens Creek) and held them in quarantine for 50 days in the Experimental Stream Laboratory at the U.S. Geological Survey (USGS) Leetown Science Center. Pre-exposure histopathology confirmed the absence of Dermocystidium infection prior to the introduction of fish into experimental enclosures.
We found that stream temperatures were warmer where sculpin have been extirpated than elsewhere in CATO where sculpin persist. However, the fish enclosure experiment revealed a positive effect of temperature on fish growth, suggesting that increased food availability and foraging rates compensated for increased metabolic demands in the warmest sites. Moreover, fish held in enclosures did not develop Dermocystidium infection. Our results therefore suggest that current environmental conditions in upper Big Hunting Creek may be suitable for Blue Ridge sculpin reintroduction, and this could ultimately lead to sportfishing opportunities by increasing the forage base for native brook trout (Salvelinus fontinalis).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201137","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Hitt, N.P., Kessler, K.G., Kelly, Z.A., Rogers, K.M., Macmillan, H.E., and Walsh, H.L., 2020, Assessing native fish restoration potential in Catoctin Mountain Park: U.S. Geological Survey Open-File Report 2020–1137, 17 p., https://doi.org/10.3133/ofr20201137.","productDescription":"Report: vii, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-122955","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":381222,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P950A13P","text":"USGS data release","linkHelpText":"Stream temperature and sculpin growth data collected from Catoctin Mountain Park in 2019"},{"id":381221,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1137/ofr20201137.pdf","text":"Report","size":"4.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1137"},{"id":381220,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1137/coverthb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Catoctin Mountain Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.51781463623047,\n 39.60621720230201\n ],\n [\n -77.38151550292969,\n 39.60621720230201\n ],\n [\n -77.38151550292969,\n 39.70137566512028\n ],\n [\n -77.51781463623047,\n 39.70137566512028\n ],\n [\n -77.51781463623047,\n 39.60621720230201\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Monitoring demographic response over time is valuable for understanding population dynamics of endangered species. We quantified the variation in survival patterns for three small isolated island populations of endangered waterfowl in the Hawaiian Archipelago. Laysan Teal Anas laysanensis were individually marked and the fate of 1,150 individuals were followed from different cohorts among the two reintroduced (Kure and Midway Atolls) and the single relict (Laysan Island) populations for time series of 4, 10 and 15 years respectively. We applied a non-parametric Kaplan-Meier estimator to describe variation between the populations in survival for different cohorts. For Laysan Island and Midway Atoll, we used log-rank tests to determine the effects of cohort, island and sex on survival. Birds in the Laysan Island population had significantly higher survival than those in the Midway population, and males had higher survival than females in both populations. The proportion of females surviving at Midway Atoll was 40% lower than for females on Laysan Island at year 5. The oldest bird observed from Laysan Island was at least 15.5 years old and had been ringed as an adult. The Kure Atoll founder cohort (n = 28) had 100% survival 18 months post-release, but this dropped by 39% during the first avian botulism type C outbreak. Ten of twenty-eight founders and a population of 60–70 birds persisted on Kure Atoll in 2020. We summarised mortality records to generate hypotheses to explain the cause-specific mechanisms driving the observed survival differences. Mortality data showed that the survival differences between islands in Laysan Teal survival was driven by chronic epizootics of avian botulism type C at Midway and Kure Atoll.
The U.S. Geological Survey (USGS) Precipitation Chemistry Quality Assurance project (PCQA) operated five distinct programs to provide external quality-assurance monitoring for the National Atmospheric Deposition Program’s (NADP) National Trends Network and Mercury Deposition Network during 2017–18. The National Trends Network programs included (1) a field audit program to evaluate sample contamination and stability, (2) an interlaboratory comparison program to evaluate analytical laboratory performance, and (3) a colocated sampler program to evaluate variability attributed to automated precipitation samplers. The Mercury Deposition Network programs include the (4) system blank program and (5) an interlaboratory comparison program. The results indicate consistently low levels of sample contamination, generally strong analytical laboratory performance, and low overall variability in concentration data imparted by field equipment. The NADP operations moved from its 40-year home at the Illinois State Water Survey to the Wisconsin State Laboratory of Hygiene in June 2018. The PCQA programs were modified and (or) temporarily curtailed during the transition in 2018. Bias and variability of sample analysis results were evaluated for the two Central Analytical Laboratories, and ongoing monitoring will be helpful to differentiate true environmental signals from the effects of changing laboratory conditions and performance. Results of quality assurance sample analyses are provided to document that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends for chemical constituents in wet deposition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205084","usgsCitation":"Wetherbee, G.A., and Martin, R., 2020, External quality assurance project report for the National Atmospheric Deposition Program’s National Trends Network and Mercury Deposition Network, 2017–18: U.S. Geological Survey Scientific Investigations Report 2020–5084, 31 p., https://doi.org/10.3133/sir20205084.","productDescription":"Report: vii, 31 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-110354","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":381227,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94RC4GD","text":"USGS data release","linkHelpText":"Data for the U.S. Geological Survey Precipitation Chemistry Quality Assurance Project for the National Atmospheric Deposition Program, 1978–2017"},{"id":381224,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5084/coverthb.jpg"},{"id":381225,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5084/sir20205084.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5084"},{"id":381226,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZKXD8N","text":"USGS data release","linkHelpText":"U.S. Geological Survey Precipitation Chemistry Quality Assurance Project Data 2017 – 2018"}],"contact":"Director, Observing Systems Division
U.S. Geological Survey
Buildings 2101, 2204 HIF
Hydrologic Instrumentation Facility
Stennis Space Center, MS 39529
Stocking of fish is an important tool for maintaining fisheries but can also significantly alter population genetic structure and erode the portfolio of within-species diversity that is important for promoting resilience and adaptability. Walleye (Sander vitreus) are a highly valued sportfish in the midwestern United States, a region characterized by postglacial recolonization from multiple lineages and an extensive history of stocking. We leveraged genomic data and recently developed analytical approaches to explore the population structure of walleye from two midwestern states, Minnesota and Wisconsin. We genotyped 954 walleye from 23 populations at ~20,000 loci using genotyping by sequencing and tested for patterns of population structure with single-SNP and microhaplotype data. Populations from Minnesota and Wisconsin were highly differentiated from each other, with additional substructure found in each state. Population structure did not consistently adhere to drainage boundaries, as cases of high intra-drainage and low inter-drainage differentiation were observed. Low genetic structure was observed between populations from the upper Wisconsin and upper Chippewa river watersheds, which are found as few as 50 km apart and were likely homogenized through historical stocking. Nevertheless, we were able to differentiate these populations using microhaplotype-based co-ancestry analysis, providing increased resolution over previous microsatellite studies and our other single SNP-based analyses. Although our results illustrate that walleye population structure has been influenced by past stocking practices, native ancestry still exists in most populations and walleye populations may be able to purge non-native alleles and haplotypes in the absence of stocking. Our study is one of the first to use genomic tools to investigate the influence of stocking on population structure in a nonsalmonid fish and outlines a workflow leveraging recently developed analytical methods to improve resolution of complex population structure that will be highly applicable in many species and systems.
","language":"English","publisher":"Wiley","doi":"10.1111/eva.13186","usgsCitation":"Bootsma, M., Miller, L., Sass, G., Euclide, P., and Larson, W., 2020, The ghosts of propagation past: Haplotype information clarifies the relative influence of stocking history and phylogeographic processes on contemporary population structure of walleye (Sander vitreus): Evolutionary Applications, v. 14, no. 4, p. 1124-1144, https://doi.org/10.1111/eva.13186.","productDescription":"21 p.","startPage":"1124","endPage":"1144","ipdsId":"IP-119896","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481105,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eva.13186","text":"Publisher Index Page"},{"id":480943,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, 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in conventional and continuous accumulations in the Upper Cretaceous Austin Chalk and Tokio and Eutaw Formations onshore and in State waters of the U.S. Gulf Coast region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203045","usgsCitation":"Pitman, J.K., Paxton, S.T., Kinney, S.A., Whidden, K.J., Haines, S.S., Varela, B.A., Mercier, T.J., Woodall, C.A., Schenk, C.J., Leathers-Miller, H.M., Pearson, O.N., Burke, L.A., Le, P.A., Birdwell, J.E., Gianoutsos, N.J., French, K.L., Drake, R.M., II, Finn, T.M., Ellis, G.S., Gaswirth, S.B., Marra, K.R., Tennyson, M.E., and Shorten, C.M., 2020, Assessment of undiscovered oil and gas resources in the Upper Cretaceous Austin Chalk and Tokio and Eutaw Formations, U.S. Gulf Coast, 2019 (ver 1.1., December 2020): U.S. Geological Survey Fact Sheet 2020–3045, 4 p., https://doi.org/10.3133/fs20203045.","productDescription":"Report: 4 p.; Data Release","onlineOnly":"N","ipdsId":"IP-114091","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":381196,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2020/3045/verHist.txt","text":"Version History","size":"4.00 KB","linkFileType":{"id":2,"text":"txt"},"description":"FS 2020-3045 version History"},{"id":379804,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F5O4TX","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project-Gulf Coast Mesozoic Province, Upper Cretaceous Austin Chalk Group and Tokio and Eutaw Formations Assessment Units and Input Data Forms"},{"id":379802,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3045/coverthb2.jpg"},{"id":379803,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3045/fs20203045.pdf","text":"Report","size":"2.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3045"}],"country":"United States","state":"Alabama, Arkansas, Louisiana, Mississippi, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.919921875,\n 27.293689224852407\n ],\n [\n -85.166015625,\n 27.293689224852407\n ],\n [\n -85.166015625,\n 35.17380831799959\n ],\n [\n -102.919921875,\n 35.17380831799959\n ],\n [\n -102.919921875,\n 27.293689224852407\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: October 29, 2020; Version 1.1: December 11, 2020","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The Upper Floridan aquifer is the uppermost, reliable aquifer in southwest Georgia. The aquifer lies on top of the Claiborne, Clayton, and Cretaceous aquifers, all of which exhibited water level declines in the 1960s and 1970s. The U.S. Geological Survey has been working cooperatively with Albany Utilities to monitor groundwater quality and availability in these aquifers since 1977.
During January 2020, nine wells were sampled—six for anions, metals, and nitrate plus nitrite as nitrogen, and three for anions, metals, and pesticides. Nitrate plus nitrite as nitrogen concentrations ranged from 2.4 milligrams per liter (mg/L) to 10.4 mg/L, and no pesticides were detected. Nitrate plus nitrite as nitrogen concentrations in well 12L277, open to the Upper Floridan aquifer, have been above the U.S. Environmental Protection Agency Maximum Contaminant Level of 10 mg/L for nitrates in drinking water since 2014.
Flow direction in the Upper Floridan aquifer is to the south and toward the Flint River. Water levels varied during the past year above and below period of record median values. Water levels in the Upper Floridan aquifer were primarily above median levels. Water levels in the Claiborne aquifer were above median levels, whereas water levels in the Clayton and Cretaceous aquifers were below median levels.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201120","collaboration":"Prepared in cooperation with Albany Utilities","usgsCitation":"Gordon, D.W., 2020, Groundwater quality and groundwater levels in Dougherty County, Georgia, April 2019 through March 2020: U.S. Geological Survey Open-File Report 2020–1120, 12 p., https://doi.org/10.3133/ofr20201120.","productDescription":"vi, 12 p.","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118275","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":380954,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1120/coverthb.jpg"},{"id":380955,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1120/ofr20201120.pdf","text":"Report","size":"1.62 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1120"}],"country":"United 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1770 Corporate Drive, Suite 500
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Nitrate, age tracer, and continuous groundwater-level data were interpreted in conjunction with airborne electromagnetic (AEM) survey data to understand the movement of nitrate within the Bazile Groundwater Management Area (BGMA) in northeastern Nebraska. Previously published age tracer data and nitrate data indicated vertical stratification of groundwater quality. Younger groundwater sampled within shallow parts of the aquifer had higher concentrations of nitrate, with 70 percent exceeding the U.S. Environmental Protection Agency maximum contaminant level of 10 milligrams per liter. In contrast, groundwater sampled from deeper parts of the aquifer indicated that nitrate concentrations were less than 2 milligrams per liter and that groundwater likely recharged prior to widespread use of commercial fertilizer.
The hydrostratigraphic interpretation of AEM profiles indicated that shallow and deep monitoring wells were often screened within the same homogenous zone of aquifer material. In contrast, test-hole logs indicated that there often are fine-grained layers within these homogenous zones that separate the shallow and deep monitoring well screens, but these fine-grained layers are not detected by the AEM technique because of decreased resolution of the AEM technique with depth.
The stratification of groundwater ages and nitrate concentrations likely was caused by groundwater-flow paths of different length, location and time of recharge, and denitrification. Within paleochannels interpreted from AEM and test-hole data, pesticides detected in groundwater generally coincide with elevated nitrate concentrations. Continuous groundwater-level data from four monitoring well nests indicated that groundwater pumping can impose or increase downward hydraulic gradients and facilitate the downward movement of nitrate into deeper parts of the High Plains aquifer. Given the density of irrigation wells within the BGMA, this effect on the hydraulic gradient is likely prevalent in other areas of the BGMA. Understanding seasonal water-level changes can allow water managers to better predict and assess the hydraulic gradient and the vulnerability of groundwater in deeper parts of the High Plains aquifer.
Nitrate, age tracer, and continuous groundwater-level data within the BGMA were interpreted in conjunction with AEM data as a case demonstration of the Nebraska Geocloud. The Nebraska Geocloud was initiated to protect taxpayer investments in AEM data collection and realize maximum benefit of these data by creating a publicly available, online digital database for long-term data storage. The Lower Platte North, Lower Platte South, Papio-Missouri River, Nemaha, Lower Loup, Central Platte, Upper Elkhorn, Lower Elkhorn, Lower Niobrara, and Lewis and Clark Natural Resources Districts; the University of Nebraska-Lincoln Conservation and Survey Division, Nebraska Natural Resources Commission, Nebraska Department of Natural Resources; and the U.S. Geological Survey entered a cooperative agreement to begin a program of data management and research aimed at understanding the best use of AEM for groundwater sustainability and management. Resulting case-study interpretations are provided to guide use of the Nebraska Geocloud to assess water-quality conditions and can be used by water managers and staff to address applicable water resource problems.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205113","collaboration":"Prepared in cooperation with the Nebraska Natural Resources Commission; Nebraska Department of Natural Resources; and Lower Platte North, Lower Platte South, Papio-Missouri River, Nemaha, Lower Loup, Central Platte, Upper Elkhorn, Lower Elkhorn, Lower Niobrara, and Lewis and Clark Natural Resources Districts","usgsCitation":"Hobza, C.M., and Steele, G.V., 2020, Interpretation of hydrogeologic data to support groundwater management, Bazile Groundwater Management Area, northeast Nebraska, 2019—A case demonstration of the Nebraska Geocloud (ver. 1.1, December 15, 2020): U.S. Geological Survey Scientific Investigations Report 2020–5113, 46 p., https://doi.org/10.3133/sir20205113.","productDescription":"Report: viii, 45 p.; Tables: 4, 5, and 6 (.xlsx and .csv); Data Release; Version 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The Sacramento splittail (Pogonichthys macrolepidotus) is composed of two genetically distinct populations endemic to the San Francisco Estuary (SFE). The allopatric upstream spawning habitat of the Central Valley (CV) population connects with the sympatric rearing grounds via relatively low salinity waters, whereas the San Pablo (SP) population must pass through the relatively high-salinity Upper SFE to reach its allopatric downstream spawning habitat. We hypothesize that if migration through SFE salinities to SP spawning grounds is more challenging for adult CV than SP splittail, then salinity tolerance, osmoregulatory capacity, and metabolic responses to salinity will differ between populations. Osmoregulatory disturbances, assessed by measuring plasma osmolality and ions, muscle moisture and Na+-K+-ATPase activity after 168 to 336 h at 11‰ salinity, showed evidence for a more robust osmoregulatory capacity in adult SP relative to CV splittail. While both resting and maximum metabolic rates were elevated in SP splittail in response to increased salinity, CV splittail metabolic rates were unaffected by salinity. Further, the calculated difference between resting and maximum metabolic values, aerobic scope, did not differ significantly between populations. Therefore, improved osmoregulation came at a metabolic cost for SP splittail but was not associated with negative impacts on scope for aerobic metabolism. These results suggest that SP splittail may be physiologically adjusted to allow for migration through higher-salinity waters. The trends in interpopulation variation in osmoregulatory and metabolic responses to salinity exposures support our hypothesis of greater salinity-related challenges to adult CV than SP splittail migration and are consistent with our previous findings for juvenile splittail populations, further supporting our recommendation of population-specific management.
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The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, 364 million gallons per day (Mgal/d) of water were withdrawn in Pointe Coupee Parish, including about 39.87 Mgal/d from groundwater sources and 323.72 Mgal/d from surface-water sources. Withdrawals for power generation accounted for 89 percent (323.98 Mgal/d) of the total water withdrawn. Withdrawals for agricultural use, composed of aquaculture, general irrigation, livestock, and rice irrigation, accounted for 8 percent (29.29 Mgal/d) of the total water withdrawn. Other categories of use included public supply, industrial, and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2014. The large increase in surface-water withdrawals from 1980 to 1985 is attributable to an increase of 262 Mgal/d for power-generation use.
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3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Elevated concentrations of nutrients and the fecal-indicator bacteria enterococci are occasionally detected in Hawai‘i’s surface waters by the State of Hawai‘i Department of Health Clean Water Branch. Management efforts to improve the water quality of surface waters are complicated by the fact that nutrients and enterococci can originate from several sources, including wastewater, animal waste, and soils. Wastewater often is the suspected source of nutrients and bacteria, but the source may not always be unequivocally identifiable from the Clean Water Branch’s routine monitoring efforts. This report—prepared in cooperation with the State of Hawai‘i Department of Health Clean Water Branch—describes a source-tracking approach for Hawai‘i that is meant to help investigators determine whether wastewater is present in the environment and where wastewater might be originating, if it is present. Wastewater sources include domestic wastewater entering the environment through on-site disposal systems and municipal wastewater entering the environment through leaky sewer systems or injection-well disposal systems. The source-tracking approach relies on the use of field-measured water properties and multiple chemical tracers of wastewater, including optical brighteners, nutrients, hydrogen and oxygen isotopes in water, nitrogen and oxygen isotopes in nitrate, organic waste compounds, and human-use pharmaceutical compounds. The source-tracking approach proposes the following sequence of steps for investigators to execute: (1) gather background information on the study area, (2) conduct trolling-instrument surveys of physical properties of surface water and identify groundwater-discharge locations, (3) collect water samples at reconnaissance sites and analyze the samples for detergent optical brighteners and specific conductance, (4) collect water samples at targeted sites and have appropriate laboratories analyze the samples for chemical tracers of wastewater, and (5) evaluate analytical results for chemical tracers of wastewater and conclude whether wastewater is present in sampled waters. The conclusions can guide management and stakeholder efforts to protect and improve the quality of Hawai‘i’s water resources.
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\"}}]}","contact":"Director, Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Mercury is an environmentally ubiquitous neurotoxin, and its methylated form presents health risks to humans and other biota, primarily through dietary intake. Because methylmercury bioaccumulates and biomagnifies in living tissue, concentrations progressively increase at higher trophic positions in ecosystem food webs. Therefore, the greatest health risks are for organisms at the highest trophic positions and for humans who consume organisms such as fish from these high trophic positions. Data on environmental mercury concentrations in various media and biota provide a basis for comparison among sites and regions and for evaluating ecosystem health risks. The U.S. Geological Survey, in cooperation with the Natural Resources Department, Bad River Band of Lake Superior Chippewa, have compiled a dataset from analyses of mercury concentrations in surface water, bed sediment, fish tissue, Rana clamitans (green frog) tissue, Haliaeetus leucocephalus (bald eagle) feathers, Lontra canadensis (North American river otter) hair, Zizania palustris (northern wild rice), and litterfall from samples collected in the Bad River watershed, Wisconsin during 2004–18. These data originated from either the Natural Resources Department or another agency based on samples collected within or near to Bad River Tribal lands before transfer to the U.S. Geological Survey for compilation and analysis at the onset of the project. This report describes the compiled mercury dataset, provides comparisons to similar measurements in the region and elsewhere, and evaluates health risks to humans and to the sampled biota. Except for litterfall, data were not collected on a consistent, regular basis over a sufficient period to evaluate temporal patterns. The reported mercury concentrations are generally similar to those reported elsewhere in the upper Great Lakes region. Reported values are consistent with atmospheric deposition as the principal source and reflect a favorable environment for mercury methylation. Fish mercury concentrations increased at higher food web positions and generally increased with length in most species measured. Sander vitreus (walleye) present the greatest risk to humans among fishes considered here because of their high trophic position and associated elevated mercury concentrations in combination with relatively high walleye consumption rates by the Native American community. Methylmercury concentrations in wild rice are generally low and likely pose little health risk. Despite reports of declining atmospheric mercury deposition across eastern North America during the past decade, a downward trend in litterfall mercury deposition was not evident in samples collected during 2012–18. Limitations in this data compilation and analysis were noted due to missing information such as collection dates and site locations for some samples. Regular monitoring of mercury in litterfall and surface waters along with periodic collection of fish would enable evaluation of temporal change in the mercury cycle that might affect future risk to humans and aquatic ecosystem inhabitants.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201095","collaboration":"Prepared in cooperation with the Natural Resources Department, Bad River Band of Lake Superior Chippewa","usgsCitation":"Burns, D.A., 2020, Compilation of mercury data and associated risk to human and ecosystem health, Bad River Band of Lake Superior Chippewa, Wisconsin (ver 1.1, December 2020): U.S. Geological Survey Open-File Report 2020–1095, 19 p., https://doi.org/10.3133/ofr20201095.","productDescription":"Report: vii, 19 p.; Database; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-110861","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":377882,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- USGS water data for the Nation"},{"id":377880,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1095/ofr20201095.pdf","text":"Report","size":"1.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1095"},{"id":377879,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1095/coverthb2.jpg"},{"id":377881,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HRS2C3","text":"USGS data release","linkFileType":{"id":5,"text":"html"},"linkHelpText":"Mercury data from the Bad River Watershed, Wisconsin, 2004–2018"},{"id":380931,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2020/1095/versionHist.txt","size":"448 B","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"Wisconsin","otherGeospatial":"Bad River Tribal Lands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.48751831054686,\n 46.54658317951774\n ],\n [\n -90.4779052734375,\n 46.57868671298067\n ],\n [\n -90.51223754882812,\n 46.599449464868584\n ],\n [\n -90.59600830078125,\n 46.63057868059483\n ],\n [\n -90.69488525390625,\n 46.69184147024343\n ],\n [\n -90.78140258789062,\n 46.71632714994794\n ],\n [\n -90.7855224609375,\n 46.66734468444288\n ],\n [\n -90.83221435546875,\n 46.62020426357956\n ],\n [\n -90.8294677734375,\n 46.57774276255591\n ],\n [\n -90.83770751953125,\n 46.39619977845332\n ],\n [\n -90.55343627929688,\n 46.409457767475764\n ],\n [\n -90.54931640625,\n 46.54280504427768\n ],\n [\n -90.48751831054686,\n 46.54658317951774\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 2020; Version 1.1: December 2020","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
The purpose of this study is to characterize streamflow availability under natural (unregulated) low-flow conditions for streams in southeast Kaua‘i, Hawai‘i. The nine main study-area basins, from north to south, include Wailua River, Hanamā‘ulu, Nāwiliwili, Pūʻali, Hulēʻia, Waikomo, Lāwaʻi, and Wahiawa Streams, and Hanapēpē River. The results of this study can be used by water managers to develop technically sound instream-flow standards for the study-area streams.
Low-flow characteristics for natural streamflow conditions were represented by flow-duration discharges that are equaled or exceeded between 95 and 50 percent of the time. Short-term continuous-record stream-gaging stations that monitored low flows on Waiahi and right branch Lāwaʻi Streams were established to serve as potential index stations for partial-record sites in the study area. Continuous-record stream-gaging station on Hanapēpē River monitored natural flow during calendar year 2017 and the streamflow record during that period was used to estimate low-flow characteristics at the station. Partial-record sites were established on 3 main streams and 15 tributary streams, upstream from existing surface-water diversions. Low-flow characteristics were determined using historical and current streamflow data from continuous-record stream-gaging stations and miscellaneous sites, as well as additional data collected as part of this study. Low-flow-duration discharges for the following streams were estimated for the 59-year base period (water years 1961–2019) using two record-augmentation techniques: right branch ʻŌpaekaʻa Stream, North Fork Wailua River, north and south fork Waikoko Streams, ‘Ili‘ili‘ula Stream, north and south fork Hanamāʻulu Streams, Kamo‘oloa Stream, Pāohia Stream, Ku‘ia Stream, Lāwa‘i Stream, Wahiawa Stream, and Hanapēpē River. The 95-percent flow-duration discharges (Q95) ranged from 0.018 to 42 cubic feet per second (ft3/s). The 50-percent flow-duration discharges (Q50) ranged from 1.1 to 69 ft3/s. Upper-bound estimates of low-flow duration discharges at partial-record sites on south fork Hanamāʻulu, Hanamāʻulu tributary, ʻŌmaʻo, and Pōʻeleʻele Streams were estimated based on the highest discharges measured as part of this study during Q95 to Q50 flow conditions, which were 0.44, 0.40, 0.19, and 0.22 ft3/s, respectively. Measured discharges on Nāwiliwili, Pū‘ali, and left branch Wahiawa Streams do not correlate with data at any active long-term continuous-record stream-gaging stations (10 or more complete water years of natural-flow record) and therefore low-flow duration discharges could not be estimated.
This study also estimated streamflow gains and losses using seepage-run discharge measurements in eight of the nine study basins (Pūʻali Stream basin was excluded). A majority of the streams gained flow downstream from the uppermost diversions. Measured seepage-gain rates ranged between 0.03 and 24.3 ft3/s per mile of stream reach. Seepage gains are presumed to originate mainly from groundwater discharge in the Wailua River, Hanamā‘ulu Stream, Nāwiliwili Stream, Hulēʻia Stream, Lāwa‘i Stream, Wahiawa Stream, and Hanapēpē River basins. Under natural-flow conditions and flow conditions of the seepage runs, a majority of the study-area streams flow continuously from the mountains to the ocean. Where a stream discharges into a reservoir––Hanamā‘ulu and Wahiawa Streams––a dry reach may occur immediately downstream from the reservoir to the point of seepage gain in the stream.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205128","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management","usgsCitation":"Cheng, C.L., 2020, Low-flow characteristics of streams from Wailua to Hanapēpē, Kauaʻi, Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2020–5128, 57 p., https://doi.org/10.3133/sir20205128.","productDescription":"viii, 57 p.","onlineOnly":"Y","ipdsId":"IP-119175","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":380936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5128/sir20205128.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5128"},{"id":380935,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5128/coverthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.35806274414062,\n 22.02072633149476\n ],\n [\n -159.43496704101562,\n 22.051277605102463\n ],\n [\n -159.554443359375,\n 22.071641456092383\n ],\n [\n -159.6258544921875,\n 22.03345683012737\n ],\n [\n -159.6533203125,\n 21.963424936844223\n ],\n [\n -159.65744018554688,\n 21.923937190109623\n ],\n [\n -159.59838867187497,\n 21.872969071537096\n ],\n [\n -159.43222045898438,\n 21.857675083878423\n ],\n [\n -159.33334350585935,\n 21.930306923001126\n ],\n [\n -159.31823730468747,\n 21.97106645968614\n ],\n [\n -159.33059692382812,\n 22.01945321869661\n ],\n [\n -159.35806274414062,\n 22.02072633149476\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pacific Islands Water Science Center
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
This report describes considerations for incorporating routine quality-assessment and quality-control evaluations into U.S. Geological Survey discrete water-sampling programs and projects. U.S. Geological Survey water-data science in 2020 is characterized by robustness, external reproducibility, collaborative large-volume data analysis, and efficient delivery of water-quality data. Confidence in data, or robustness, can be increased by supplementing traditional field-based quality-control data with laboratory quality control (QC) data, such as third-party blind spikes and blind blanks, laboratory blanks, and laboratory-reagent spikes. Laboratory quality-control data can provide additional information about bias and variability, method performance, and false-positive and false-negative rates that are not available from field QC data alone. Reproducibility is supported by means of standardizing metadata and documentation. Collaborative analysis brings together disparate elements of various types of quality-control review and communicates persistent data quality issues for compounds to data users internal and external to the U.S. Geological Survey. Efficient delivery of water-quality data is achieved when quality-control review is accomplished in the same expedited (near real-time) time frame as distribution of environmental results to the public and might be improved with consideration given to data versioning or to a system of alerting data users to data interpretation that might differ from originally published data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201109","usgsCitation":"Medalie, L., 2020, Considerations for incorporating quality control into water quality sampling strategies for the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2020–1109, 5 p., https://doi.org/10.3133/ofr20201109.","productDescription":"iii, 5 p.","numberOfPages":"5","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120022","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":380650,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1109/coverthb.jpg"},{"id":380651,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1109/ofr20201109.pdf","text":"Report","size":"935 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1109"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Water samples from 122 sites in the U.S. Geological Survey National Water Quality Network were collected in 2013–17 to document ambient water-quality conditions in surface water of the United States and to determine status and trends of loads and concentrations for nutrients, contaminants, and sediment to estuaries and streams. Quality-control (QC) samples collected in the field with environmental samples were combined with QC samples from laboratory processing to provide information and documentation about the quality of the environmental data.
Quality assurance for inorganic and organic compounds assessed in the National Water Quality Network includes collection of field blanks to determine contamination bias and field replicates to determine variability bias. No contamination bias was found for 6 of the 13 nutrient compounds analyzed, and some potential contamination bias for some years was found for the other 7 nutrient compounds. Contamination bias was not found for carbon compounds or ultraviolet-absorbance measurements and was not assessed for sediment. All major ions and trace elements except potassium and lithium showed moderate contamination bias for at least 1 water year; generally, this bias was not at environmentally relevant concentrations. All compounds in the nutrient, carbon, and sediment group and in the major ions and trace elements group had low variability both in detection frequency and in concentration. Exceptions to this low variability were total particulate inorganic carbon and sediment for 2015, both of which are particulate substances with intrinsically high sampling variability.
The risk of contamination bias for pesticides in National Water Quality Network samples was low, as indicated by very few detections in field blanks. Sixteen pesticide compounds showed potential contamination bias based on unexpected detections in third-party blind spikes (false-positive results for compounds that are not included in the spike mixture of a sample, where the identity as a QC sample is unknown to the analyst), and 47 different compounds (out of 225 pesticide compounds) showed potential contamination bias from laboratory blanks. However, when timing and relative magnitudes of detections in blank samples, environmental samples, and benchmark concentrations are considered, most of this potential contamination is not relevant to interpretation of published pesticide results. Overall variability in detection frequency for pesticides from field replicates was low or moderate. Also based on field replicates, 55 pesticides had overall high variability in concentrations for at least 1 water year, although these assessments likely overestimate high variability.
At least 1 QC issue was found for 87 pesticides; however, most of the QC issues had no or little effect on the interpretation of environmental results because the U.S. Geological Survey National Water Quality Laboratory addressed the QC issue before publishing the environmental results, environmental results were almost entirely nondetections, concentrations of environmental results were higher than potential contamination bias, or benchmark concentrations were orders of magnitude higher than all environmental results. Eight compounds affected by two QC issues had a benchmark less than 100 nanograms per liter and warranted careful consideration of timing and magnitude of QC results in relation to surface-water results before interpretive use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205116","usgsCitation":"Medalie, L., and Bexfield, L.M., 2020, Quality of data from the U.S. Geological Survey National Water Quality Network for water years 2013–17: U.S. Geological Survey Scientific Investigations Report 2020–5116, 21 p., https://doi.org/10.3133/sir20205116.","productDescription":"Report: v, 21 p.; Data Releases; 9 Tables","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-115536","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":436706,"rank":17,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94F31R8","text":"USGS data release","linkHelpText":"Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 1963-2018"},{"id":380873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5116/coverthb2.jpg"},{"id":380874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116.pdf","text":"Report","size":"1.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5116"},{"id":380877,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90BFKA4","text":"USGS data release","linkHelpText":"Field, laboratory, and third-party data for assessment of the quality of pesticide results reported by the National Water Quality Laboratory for groundwater samples collected by the National Water-Quality Assessment Project, 2013–18"},{"id":380878,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96VY980","text":"USGS data release","linkHelpText":"Field blank and field replicate datasets for inorganic and organic compounds collected for the National Water Quality Network, 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.
Neonicotinoids are one of the most widely used classes of insecticides in the world. The neonicotinoid imidacloprid is commonly applied to hemlock (Tsuga spp.) stands in eastern North America to reduce tree mortality from infestations of the invasive hemlock woolly adelgid (HWA; Adelges tsugae). While laboratory and mesocosm studies have determined that imidacloprid can bioaccumulate in anurans and cause sublethal effects, no field studies have investigated whether salamanders or insects in streams adjacent to HWA treatments bioaccumulate imidacloprid or if sublethal effects are detectable in wild salamanders. We assessed relationships between imidacloprid exposure and stream salamander health in West Virginia, USA, using concentration of the stress hormone corticosterone and body condition indices (BCI) as response variables. Of 107 Desmognathus salamanders from 11 sites tested for bioaccumulation, we detected imidacloprid in 47 salamanders. Of 15 benthic macroinvertebrate samples tested, we detected imidacloprid, imidacloprid-urea, and imidacloprid-olefin in 15, 13, and 1 sample, respectively. Based on 115 Desmognathus salamanders sampled at 11 sites for stress hormone responses, corticosterone concentration increased with imidacloprid concentration in stream water. For 802 salamanders sampled at 48 sites, BCI decreased as concentration of imidacloprid in stream water increased, but explanatory power was low. Our study suggests that chronic leaching of imidacloprid from treated hemlock stands into adjacent streams has the potential to negatively affect aquatic organisms and may provide a route of exposure to higher trophic levels.
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User guide for the Gulf Coast Monitoring and Assessment Portal","interactions":[],"lastModifiedDate":"2022-11-30T17:43:42.959404","indexId":"70238586","displayToPublicDate":"2020-12-01T11:35:58","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Council Monitoring and Assessment Program (CMAP): User guide for the Gulf Coast Monitoring and Assessment Portal","docAbstract":"The Resources and Ecosystems Sustainability, Tourist Opportunities, and Revived Economies of the Gulf Coast States Act (RESTORE Act Final Rule at 31 C.F.R. 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The Council plays a key role in developing strategies and implementing projects that help ensure the Gulf of Mexico’s natural resources are sustainable and available for future generations. \n\nThe Council Monitoring and Assessment Program (CMAP) was approved as a Gulf-wide investment by the Council in the 2015 Initial Funded Priorities List and is administered jointly by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Geological Survey (USGS). Funded activities include the organization of basic, foundational components for a Gulf-wide monitoring network to measure the efficacy of investments in Gulf restoration by the Council. The program, in coordination with the Gulf of Mexico Alliance (GOMA) and through collaboration with the Gulf States, federal and local partners, academia, non-governmental organizations, and business and industry, has leveraged existing resources, capacities, and expertise and built on existing monitoring programs and their data.","language":"English","publisher":"NOAA","collaboration":"National Oceanic and Atmospheric Administration, Gulf Coast Ecosystem Restoration Council, University of Louisiana","usgsCitation":"Suir, K., Thurman, H., Kuczynski, A., Quibodeaux, P., Chimmula, S., Howell, J., Burkart, H., Enwright, N., Cretini, K., and McKelvy, M., 2020, Council Monitoring and Assessment Program (CMAP): User guide for the Gulf Coast Monitoring and Assessment Portal, 20 p.","productDescription":"20 p.","ipdsId":"IP-125322","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research 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mckelvym@usgs.gov","orcid":"https://orcid.org/0000-0001-5465-2571","contributorId":4865,"corporation":false,"usgs":true,"family":"McKelvy","given":"Mark","email":"mckelvym@usgs.gov","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":858046,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70228275,"text":"70228275 - 2020 - Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu","interactions":[],"lastModifiedDate":"2022-02-08T16:10:19.163337","indexId":"70228275","displayToPublicDate":"2020-12-01T09:47:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu","title":"Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu","docAbstract":"Ecological risk assessments play an important role in environmental management and decision-making. Although empirical measurements of the effects of habitat changes and chemical exposure are often made at molecular and individual levels, environmental decision-making often requires the quantification of management-relevant, population-level outcomes. In this study, we generalized a modeling framework to evaluate population-level ecological risk of environmental stress and bioactive chemicals. The modeling framework includes (1) a biological model module that incorporates complex and interacting biological and ecological processes, and environmental stochasticity, (2) an effect module that links the impacts of environmental changes and chemical exposure to individual characteristics, and (3) a population module that makes decisions on the choice of population-level properties to best capture the effects and thus to track in the model based on the target species and the research and management interest. This framework is a 3-module procedure that provides an alternative way for researchers to organize, present and communicate the risk assessment modeling studies. To demonstrate this framework, we used a socioeconomically important riverine fish species, smallmouth bass Micropterus dolomieu, as the model species. We developed an individual-based model as the biological model module. We evaluated the impacts of changing water temperature and flow regimes, and the impacts of exposure to estrogenic endocrine disrupting compounds (EEDC) on smallmouth bass populations in the Chesapeake Bay Watershed, USA. Warm summer water temperatures and year-round high flows had the most severe impacts on the smallmouth bass population. An increase in exposure level to EEDC, both year-round and in summer months, substantially reduced population size, spawner and recruit abundance, and the proportion of quality-length individuals. Acute exposure to EEDC was more detrimental to the population than chronic exposure. Acute exposure during spawning season had the most severe impacts. This modeling framework can be extended to other species, environmental factors and chemicals, and can be used to inform management and conservation decisions.
Telemetry of acoustically tagged bigheaded carp (i.e., bighead carp Hypophthalmichthys nobilis and silver carp H. molitrix) and surrogate fish species has become an invaluable tool in management for these species in the upper Illinois Waterway Systems (i.e., upper Illinois River, lower Des Plaines River, and Chicago Area Waterway System). For example, movement probabilities between adjacent navigation pools need to be estimated to parameterize the Spatially Explicit Asian Carp Population Model (SEAcarP). SEAcarP is a population model used in scenario planning by the Monitoring and Response Workgroup (MRWG) to evaluate alternative management actions. These movement probabilities are estimated from the telemetry data obtained from a longitudinal network of strategically placed receivers that detect bigheaded carp that have been implanted with acoustic transmitters. In addition, fish removal by contracted fishers has become the primary method of controlling bigheaded carp in the upper Illinois and lower Des Plaines Rivers. Variable patterns in bigheaded carp distribution, habitat, and movement, influenced by seasonal and environmental conditions, make targeting bigheaded carp for removal and containment challenging and costly. Understanding these movement patterns for bigheaded carp through modeling and real-time telemetry applications informs removal efforts and facilitates monitoring and contingency actions based on fish movements.
To develop a better understanding of fish movement dynamics to meet management objectives, an existing network of real-time and data-logging acoustic receivers in the upper Illinois Waterway Systems is collaboratively managed by a multi-agency team (see Participating Agencies section above). A Telemetry Workgroup has been established by the MRWG to ensure that the multi-agency telemetry efforts are coordinated to efficiently and effectively meet the MRWG goals. This workgroup plans and executes the placement of receivers, tagging of bigheaded carp with acoustic tags, and management of the telemetry data. Three primary objectives to meet MRWG goals identified by the Telemetry Workgroup included (1) development of a common standardized telemetry database with visualization and analysis tools, (2) transitioning from Program MARK (http://www.phidot.org/software/mark/) to a custom Bayesian multi-state model for estimating movement probabilities needed for SEAcarP and (3) deploying, maintaining, and serving data from real-time acoustic receivers to inform contingency planning and fish removal.
A telemetry database and visualization tools (FishTracks) will facilitate standardization, archiving, sharing, quality assurance, visualization and analysis of the telemetry data needed for management. Modifications and additions to FishTracks will facilitate more problem-free use of the database and associated applications, as well as useful extraction of information to meet management goals. The transition to a custom Bayesian multi-state model to estimate movement probabilities will support more efficient, effective, and robust population modeling with SEAcarP by overcoming short comings of Program MARK for this purpose. These shortcomings include lack of customizability and extensibility, problems of singularities and poor-convergence, software crashes, parameter exclusion from models, an inability to consistently generate estimates of movement probability, and a lack of uncertainty estimates for movement probabilities. A real-time receiver network that is maintained and tested annually will ensure reliability and accuracy of the real-time alerts to bigheaded carp movements that can be used by management to plan contingency actions.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Interim summary report 2020","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Asian Carp Regional Coordinating Committee","usgsCitation":"Knights, B.C., Brey, M.K., Stanton, J.C., Harrison, T.J., Appel, D., Hlavacek, E., and Duncker, J.J., 2020, USGS Telemetry Project: Interim Summary Report, 6 p.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-128212","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":391250,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html"},{"id":426889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"upper Illinois Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.34655836479192,\n 42.36183831544582\n ],\n [\n -89.74494117447833,\n 42.36183831544582\n ],\n [\n -89.74494117447833,\n 40.51767088873834\n ],\n [\n -87.34655836479192,\n 40.51767088873834\n ],\n [\n -87.34655836479192,\n 42.36183831544582\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826139,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826140,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826141,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826142,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Appel, Douglas 0000-0001-8775-1058","orcid":"https://orcid.org/0000-0001-8775-1058","contributorId":268159,"corporation":false,"usgs":true,"family":"Appel","given":"Douglas","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826143,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hlavacek, Enrika 0000-0002-9872-2305 ehlavacek@usgs.gov","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":149114,"corporation":false,"usgs":true,"family":"Hlavacek","given":"Enrika","email":"ehlavacek@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826144,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duncker, James J. 0000-0001-5464-7991 jduncker@usgs.gov","orcid":"https://orcid.org/0000-0001-5464-7991","contributorId":4316,"corporation":false,"usgs":true,"family":"Duncker","given":"James","email":"jduncker@usgs.gov","middleInitial":"J.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826145,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70248275,"text":"70248275 - 2020 - Changing nitrogen inputs to the northern San Francisco Estuary: Potential ecosystem responses and opportunities for investigation","interactions":[],"lastModifiedDate":"2023-09-08T13:41:09.716807","indexId":"70248275","displayToPublicDate":"2020-12-01T08:33:32","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesTitle":{"id":16708,"text":"SFEI Contribution","active":true,"publicationSubtype":{"id":4}},"seriesNumber":"973","title":"Changing nitrogen inputs to the northern San Francisco Estuary: Potential ecosystem responses and opportunities for investigation","docAbstract":"Anthropogenic activities have resulted in elevated ambient nitrogen (N) and phosphorus (P) concentrations in many regions of the Sacramento-San Joaquin Delta and Suisun Bay (northern San Francisco Estuary, (nSFE). The Sacramento Regional wastewater treatment plant (SRWTP WWTP) currently acts as the largest N point source to the system, discharging 13,000-15,000 kg/d of ammonium-N (NH4) near the nSFE’s northeastern boundary. By end of 2021, SRWTP will complete major upgrades that will reduce its effluent dissolved inorganic nitrogen (DIN) loads by >65% and release the remaining DIN as predominantly nitrate (NO3). This major change in nitrogen inputs provides a unique opportunity to study ecosystem-scale responses to an altered nutrient regime. While, in general, the nSFE has not experienced some classic symptoms of nutrient over-enrichment typical in other estuaries—e.g., large phytoplankton blooms and hypoxia—other concerning nutrient- related impacts have been hypothesized, including: occurrence of harmful algal blooms (HABs) and the production of cyanotoxins; excessive growth of invasive aquatic vegetation; and declines in the abundance and nutritional quality of phytoplankton. These impacts have repercussions for the system’s food web, habitat quality, and the way we manage for transportation, recreation, water conveyance, and drinking water quality. This report develops and applies a framework for i) identifying and examining ecosystem response scenarios to the forthcoming decreased N loads; and ii) identifying opportunities, and constraints or considerations, for investigating those responses, including key data needs or knowledge gaps. Through applying this framework, we identify a set of plausible response scenarios, and evaluate the feasibility of studying or observing those responses along with key study considerations and data and knowledge gaps.","language":"English","publisher":"Bay Area Clean Water Agencies","usgsCitation":"Senn, D., Kraus, T.E., Richey, A., Bergamaschi, B.A., Brown, L.R., Conrad, L., Francis, C.A., Kimmerer, W., Kudela, R., Otten, T.G., Parker, A.E., Robinson, A., Mueller-Solger, A., Stern, D., and Thompson, J., 2020, Changing nitrogen inputs to the northern San Francisco Estuary: Potential ecosystem responses and opportunities for investigation: SFEI Contribution 973, 52 p.","productDescription":"52 p.","ipdsId":"IP-117945","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":420537,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://bacwa.org/","linkFileType":{"id":5,"text":"html"}},{"id":420664,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.24778769231813,\n 38.71083341899933\n ],\n [\n -122.19532072938517,\n 38.71083341899933\n ],\n [\n -122.19532072938517,\n 37.68818730495191\n ],\n [\n -121.24778769231813,\n 37.68818730495191\n ],\n [\n -121.24778769231813,\n 38.71083341899933\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Senn, David","contributorId":177368,"corporation":false,"usgs":false,"family":"Senn","given":"David","affiliations":[],"preferred":false,"id":882203,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kraus, Tamara E. 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Water-level altitudes were calculated based on the measured depth to water in each well and used to construct a potentiometric-surface map of the Mississippi River Valley alluvial aquifer, and the water-quality samples were analyzed for chloride and bromide concentrations. Upon completion of the potentiometric-surface map, 10 depressions in the potentiometric surface were identified in the Mississippi Alluvial Plain: two large depressions, five small depressions, and three areas of decreased water levels. Analyses of water-quality samples identified several areas of elevated chloride/bromide ratios.
A water-level altitude difference map was constructed using 345 groundwater levels measured in 2010 and 2014. Differences in water-level altitude ranged from –10.2 feet in Craighead County to 18.00 feet in Prairie County. Analysis of the overall water-level altitude differences indicated a decline in approximately 84 percent of the wells measured in both 2010 and 2014, including in areas where previous studies indicated water-level altitude increases between 2008 and 2012. Analysis of long-term hydrographs of wells in the study area indicated that mean annual water levels declined in all but two counties. The decline in water levels observed in the hydrographs suggests continued growth of the cones of depression caused by groundwater use in the Mississippi River Valley alluvial aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205123","collaboration":"Prepared in cooperation with the Arkansas Natural Resources Commission and the Arkansas Geological Survey","usgsCitation":"Rodgers, K.D., and Whaling, A.R., 2020, Water levels and selected water-quality conditions in the Mississippi River Valley alluvial aquifer in eastern Arkansas, 2014: U.S. Geological Survey Scientific Investigations Report 2020–5123, 22 p., 3 pls., https://doi.org/10.3133/sir20205123.","productDescription":"Report: v, 22 p.; 3 Plates: 20.04 x25.98 inches or smaller; Data Release","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082198","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":380660,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5123/coverthb.jpg"},{"id":380661,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5123/sir20205123.pdf","text":"Report","size":"962 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5123"},{"id":380662,"rank":3,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5123/sir20205123_plate1.pdf","text":"Plate 1","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5123 Plate 1","linkHelpText":"— Potentiometric Surface of the Mississippi River Valley Alluvial Aquifer, Spring 2014"},{"id":380664,"rank":5,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5123/sir20205123_plate3.pdf","text":"Plate 3","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5123 Plate 3","linkHelpText":"— Chloride/Bromide Ratio of the Mississippi River Valley Alluvial Aquifer, Spring 2014–15"},{"id":380663,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5123/sir20205123_plate2.pdf","text":"Plate 2","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5123 Plate 2","linkHelpText":"— Difference in Water Level of the Mississippi River Valley Alluvial Aquifer, 2010–14"},{"id":380665,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F757197T","text":"USGS data release","description":"USGs data Release","linkHelpText":"Water-level data, selected water-quality data, and the potentiometric dataset for the Mississippi River Valley alluvial aquifer in eastern Arkansas, spring 2014"}],"country":"United States","state":"Arkansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.85693359375,\n 36.527294814546245\n ],\n [\n -91.62597656249999,\n 35.24561909420681\n ],\n [\n -92.21923828124999,\n 34.74161249883172\n ],\n [\n -91.8017578125,\n 33.94335994657882\n ],\n [\n -91.62597656249999,\n 33.22949814144951\n ],\n [\n -91.60400390625,\n 33.04550781490999\n ],\n [\n -91.01074218749999,\n 33.08233672856376\n ],\n [\n -90.72509765625,\n 34.07086232376631\n ],\n [\n -90.3076171875,\n 34.88593094075317\n ],\n [\n -89.97802734375,\n 35.460669951495305\n ],\n [\n -89.67041015625,\n 36.03133177633187\n ],\n [\n -90.3076171875,\n 36.049098959065645\n ],\n [\n -90.06591796875,\n 36.33282808737917\n ],\n [\n -90.15380859375,\n 36.491973470593685\n ],\n [\n -90.52734374999999,\n 36.50963615733049\n ],\n [\n -90.85693359375,\n 36.527294814546245\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