At sites that have been sampled for decades, changes in field and laboratory methods happen over time as instrumentation and protocols improve. Here, we compare the influence of depth- and point-integrated sampling on total, fine (< 0.0625 mm), and coarse (≥ 0.0625 mm) suspended sediment (SS) concentrations in the Lower Mississippi and Atchafalaya Rivers. Using historical field method information, we identified seven sites to test such differences. We found SS samples collected using point-integration tended to have higher concentrations than those collected using depth-integration. However, the presence and magnitude of the bias were inconsistent across sites. Bias was present at the site with less-than-ideal conditions (i.e., non-trapezoidal channel, non-uniform flow) and non-existent at the ideal site location, indicating the bias between sampling methods depends on site sampling conditions. When present, the bias is greater at higher concentrations and at moderate to high flows. At the less-than-ideal site, point-integrated samples can have 16% (total) and 34% (coarse) higher concentrations than depth-integrated samples. When flow effects are removed, this translates to a bias of 19, 9, and 8 mg per liter for total, fine, and coarse SS. When a change in field methods occurs, comparison samples and a rigorous evaluation of those samples are warranted to determine the proper course of action for a particular site. Often, the effect and solution will not be known until several years of comparison samples have been collected under a variety of hydrologic conditions.
J.N. “Ding” Darling National Wildlife Refuge (DDNWR) is located on Sanibel Island along the southwestern coast of Florida, USA. Sanibel Island is heavily developed, but DDNWR provides protection for a large mangrove area that supports biodiversity and recreational opportunity. However, nitrogen (N) and phosphorus (P) eutrophication attributed to agriculture discharge along the Caloosahatchee River has affected the area’s aquatic habitat with algal blooms and may be causing untimely degradation of Sanibel’s mangrove forests. We launched a series of studies to understand how additional nutrient loading to the levels expected in the future might affect DDNWR’s mangrove resource. We experimentally fertilized selected mangrove forest areas with N fertilizer (+N; NH4) and P fertilizer (+P; P2O5) for three years, and monitored soil surface elevation change, soil and pneumatophore CO2 fluxes from respiration, mangrove tree sap flow from two species (Avicennia germinans, Rhizophora mangle), and individual tree and stand water use, from which we developed carbon (C) budgets for +N and +P vs. control simulations as applied to DDNWR’s 1112 ha mangrove area. Many of the measured response variables provided hints of subtle changes in response to +P rather than +N, which were compounded when scaled. From this, we found that additional P loading is expected to stimulate CO2 uptake via net ecosystem exchange of C, likely pressing the system beyond metabolic capacity and leading to a projected 41% increase in lateral C export to the estuary. Additional lateral C export is concomitant to a reduction in vertical soil surface elevation with +P. Furthermore, an inability of DDNWR’s mangroves to bury additional P and a release of P-bound ions to lateral export may exacerbate estuarine eutrophication. We also modelled the effect of sea-level rise influences on DDNWR’s mangroves through 2100 using a soil cohort model (WARMER-Mangroves) and found that the mangroves may be resilient to current rates of sea-level rise into the future but may also be susceptible to moderate accelerations. Greater eutrophication could create additional vulnerabilities to mangrove submergence, especially to basin mangroves where P concentrations are high and already reducing soil surface elevations in some mangroves. Our results suggest that amelioration of current P concentrations and avoidance of additional P loading to Sanibel Island’s mangroves are management options to consider.
","language":"English","publisher":"Southeast Climate Adaptation Science Center","usgsCitation":"Krauss, K., Conrad, J.R., Duberstein, J., Ward, E., Drexler, J.Z., Buffington, K., Thorne, K., Benscoter, B.W., Miller, H., Faron, N.T., Merino, S., From, A., Peneva-Reed, E., and Zhu, Z., 2023, Mangrove habitat persistence and carbon vulnerability associated with increased nutrient loading and sea-level rise at Ding Darling National Wildlife Refuge (Sanibel Island, Florida, USA), 46 p.","productDescription":"46 p.","ipdsId":"IP-156801","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":427403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":427371,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://secasc.ncsu.edu/science/mangrove-ecosystem-services/"}],"country":"United States","state":"Florida","otherGeospatial":"Ding Darling National Wildlife Refuge, Sanibel Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.09257743948284,\n 26.43591715803987\n ],\n [\n -82.04970400948879,\n 26.44673081335216\n ],\n [\n -82.05393096737532,\n 26.472138907162602\n ],\n [\n -82.08170811920239,\n 26.469976734538434\n ],\n [\n -82.09016203497596,\n 26.461327637778552\n ],\n [\n -82.1227699958168,\n 26.475922611484222\n ],\n [\n -82.15718951003726,\n 26.494839266177138\n ],\n [\n -82.17107808595081,\n 26.49916263597524\n ],\n [\n -82.17590889496402,\n 26.515914157596868\n ],\n [\n -82.18315510848416,\n 26.521857658684468\n ],\n [\n -82.18557051299105,\n 26.487272977733056\n ],\n [\n -82.14330093412373,\n 26.455381006749434\n ],\n [\n -82.10163520638315,\n 26.43916136120143\n ],\n [\n -82.09257743948284,\n 26.43591715803987\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":219804,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898085,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conrad, Jeremy R.","contributorId":149347,"corporation":false,"usgs":false,"family":"Conrad","given":"Jeremy","email":"","middleInitial":"R.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":898086,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Duberstein, Jamie A.","contributorId":91007,"corporation":false,"usgs":false,"family":"Duberstein","given":"Jamie A.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":898087,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ward, Eric 0000-0002-5047-5464","orcid":"https://orcid.org/0000-0002-5047-5464","contributorId":217389,"corporation":false,"usgs":true,"family":"Ward","given":"Eric","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":898088,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":5044,"text":"National Research Program - 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2023 - Lake Ontario April prey fish survey results and Alewife assessment, 2023","interactions":[],"lastModifiedDate":"2025-08-26T14:29:06.466685","indexId":"70249606","displayToPublicDate":"2023-10-01T10:08:53","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Lake Ontario April prey fish survey results and Alewife assessment, 2023","docAbstract":"The April bottom trawl survey and Alewife Alosa pseudoharengus population assessment provides science to inform Lake Ontario fisheries management. The 2023 survey included 215 trawls in the main lake and embayments, and sampled depths from 6.5 to 252 m (21-833 ft). The survey captured 1,012,178 fish from 32 species with a total weight of 12,136 kg (26,700 lbs.). Alewife were 92% of the catch by number while Rainbow Smelt, Osmerus mordax, Deepwater Sculpin, Myoxocephalus thompsonii, and Round Goby, Neogobius melanostomus, comprised 3%, 3%, and 1% of the catch, respectively. To improve the accuracy of prey fish biomass and density estimates we reanalyzed trawl sensor data from each of three participating survey vessels and created vessel-specific relationships predicting how bottom trawl bottom contact time, wing width, and area-swept varies with depth.
Total Alewife biomass increased in 2023 due to growth and survival of the abundant 2020 year class (now age-3) and an abundant 2022 year class (age-1). The 2023 mean Alewife biomass (81.1 kg·ha-1) was the largest since whole lake sampling began in 2016 and was the ninth largest value observed in the modern time series (1997-2023, maximum value in 2000 = 91.8 kg·ha-1). The 2023 Alewife density (6795 n·ha-1) was the greatest density observed in the modern time series. These high biomass and density values are due to above average Alewife reproductive success in 2020 and 2022. Simulation modeling suggests the 2024 and 2025 Alewife biomass index may be substantially higher than the 2023 observations.
In 2023, the Rainbow Smelt biomass index increased relative to the 2022 index, as did the biomass index for Cisco, Coregonus artedi. In contrast, Emerald Shiner Notropis atherinoides and Threespine Stickleback Gasterosteus aculeatus, biomass values continue to be low (< 0.01 kg·ha-1). Three Bloater Coregonus hoyi, were captured during the 2023 survey. Hydroacoustic sampling conducted during the bottom trawl survey estimated prey fish densities in pelagic habitats not sampled by the bottom trawl (3 m below the surface to 3 m above the lake bottom) and these densities were hundreds to thousands of times lower than bottom trawl-based densities. These results support the idea that, in April, when the warmest water is on the lake bottom, Alewife and most other pelagic prey fish are near the lake bottom and can be effectively sampled with bottom trawling.
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Center","active":true,"usgs":true}],"preferred":true,"id":886440,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70250110,"text":"70250110 - 2023 - Stable isotope constraints on the source of ore fluids for the Hicks Dome REE+Y-HFSE-fluorspar deposit","interactions":[],"lastModifiedDate":"2023-11-20T16:11:18.374145","indexId":"70250110","displayToPublicDate":"2023-10-01T10:04:32","publicationYear":"2023","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Stable isotope constraints on the source of ore fluids for the Hicks Dome REE+Y-HFSE-fluorspar deposit","docAbstract":"Hicks Dome is comprised of coarse crystalline Mississippi Valley Type deposits at shallow levels and an enigmatic, fine-grained fluorite, rare earth elements, Y, high field strength elements, Be, and Ba rich deposit at deeper levels. Phyllosilicates from a lamprophyre dike and a breccia from two Hicks Dome drill cores were sampled to resolve the fluid history of the entire deposit using light stable isotopes. Silicate fluorination coupled with isotope ratio mass spectrometry give δ18O values from +6.9 to +16.0 ‰ (Vienna Standard Mean Ocean Water). Temperature conversion elemental analyzer and gas chromatography-isotope ratio mass spectrometry give δ2H values from -54 to -33 ‰ (Vienna Standard Mean Ocean Water). Muscovite from metasomatized dikes and breccias are relatively enriched in 18O compared to phlogopite from lamprophyre. Calculated isotopic compositions of the fluids from which the phyllosilicates precipitated indicate that phlogopite retained a magmatic composition while muscovite likely formed from magmatic fluids that exchanged with carbonate host rocks or from magmatic fluids that mixed with basinal brines. Enrichment of deuterium in fluids calculated from muscovite suggest that fluids were derived from hypothesized carbonatites or were acidic. 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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carpenter, Kurt D. 0000-0002-6231-8335 kdcar@usgs.gov","orcid":"https://orcid.org/0000-0002-6231-8335","contributorId":127442,"corporation":false,"usgs":true,"family":"Carpenter","given":"Kurt","email":"kdcar@usgs.gov","middleInitial":"D.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":893658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Chauncey W. 0000-0002-1016-3781 chauncey@usgs.gov","orcid":"https://orcid.org/0000-0002-1016-3781","contributorId":140160,"corporation":false,"usgs":true,"family":"Anderson","given":"Chauncey","email":"chauncey@usgs.gov","middleInitial":"W.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":893659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sobota, Daniel","contributorId":333712,"corporation":false,"usgs":false,"family":"Sobota","given":"Daniel","email":"","affiliations":[{"id":27064,"text":"Oregon Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":893660,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249237,"text":"70249237 - 2023 - A general approach for evaluating of the coverage, resolution, and representation of streamflow monitoring networks","interactions":[],"lastModifiedDate":"2023-10-02T12:00:42.948235","indexId":"70249237","displayToPublicDate":"2023-09-30T06:57:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1552,"text":"Environmental Monitoring and Assessment","onlineIssn":"1573-2959","printIssn":"0167-6369","active":true,"publicationSubtype":{"id":10}},"title":"A general approach for evaluating of the coverage, resolution, and representation of streamflow monitoring networks","docAbstract":"Streamflow monitoring networks provide information for a wide range of public interests in river and streams. A general approach to evaluate monitoring for different interests is developed to support network planning and design. The approach defines three theoretically distinct information metrics (coverage, resolution, and representation) based on the spatial distribution of a variable of interest. Coverage is the fraction of information that a network can provide about a variable when some areas are not monitored. Resolution is the information available from the network relative to the maximum information possible given the number of sites in the network. Representation is the information that a network provides about a benchmark distribution of a variable. Information is defined using Shannon entropy where the spatial discretization of a variable among spatial elements of a landscape or sites in a network indicates the uncertainty in the spatial distribution of the variable. This approach supports the design of networks for monitoring of variables with heterogeneous spatial distributions (“hot spots” and patches) that might otherwise be unmonitored because they occupy insignificant portions of the landscape. Areas where monitoring will maintain or improve the metrics serve as objective priorities for public interests in network design. The approach is demonstrated for the streamflow monitoring network operated by the United States Geological Survey during water year 2020 indicating gaps in the coverage of coastal rivers and the resolution of low flows.
The lack of contemporary data on contaminants in resident fish prevents an analysis of temporal trends in contaminant concentrations and the present-day status of the “Restrictions on Fish and Wildlife Consumption” Beneficial Use Impairment (BUI) in the Niagara River Area of Concern (AOC). During 2018, concentrations of 260 contaminants in four groups of fish species from five areas in or near the upper Niagara River AOC were analyzed to determine the potential risks from fish consumption, responses to remediation in an adjacent AOC, and whether additional remedial actions were warranted in this AOC. The concentrations of most contaminants were generally undetectable or did not exceed established Federal, State, or European Union fish-consumption limits. Several contaminants without State or Federal limits exceeded European Union fish-consumption guidelines in some species and areas. High PCB residues in many bullhead from Scajaquada Creek, some bass from Hoyt Lake, and most common carp from all areas surpassed certain New York and Federal fish-consumption limits and indicate PCBs continue to restrict fish consumption at most study areas. High concentrations of several contaminants in resident fish, especially dioxins and furans in carp, from Cayuga Creek indicate that additional scrutiny of consumption advisories is warranted in this area. Because this study provides a snapshot in time of fish-contaminant data from five areas, additional data from other areas and times, and more detailed information on present-day pollutant sources may be needed before determining the status of the fish-consumption BUI in this AOC.
Current methods for determining the 1-percent annual exceedance probability (AEP) for a streamflow assume stationarity (the assumption that the statistical distribution of data from past observations does not contain trends and will continue unchanged in the future). This assumption allows the 1-percent AEP to be determined based on historical streamflow records. However, the assumption of stationarity is challenged by observed trends in streamflow records.
In response, the U.S. Geological Survey, in cooperation with the Federal Emergency Management Agency, studied potential changes to the 1-percent AEP streamflows at streamgages in the Housatonic River watershed in Massachusetts, Connecticut, and New York. The study used the Precipitation-Runoff Modeling System—a deterministic hydrologic model. Climate inputs to the model of temperature and precipitation were scaled to anticipated changes based on global climate models that could occur in 2030, 2050, and 2100. The model outputs were used to characterize the 1-percent AEP streamflows for 2030, 2050, and 2100 and compare the results to baseline conditions for 1950 to 2015. Results indicated that the 1-percent AEP streamflow for unregulated streams and rivers may increase from the 1950–2015 baseline period by 7.4, 11.7, and 17.3 percent in 2030, 2050, and 2100, respectively, because of climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235090","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Olson, S.A., 2023, Characterizing changes in the 1-percent annual exceedance probability streamflows for climate-change scenarios in the Housatonic River watershed of Massachusetts, Connecticut, and New York: U.S. Geological Survey Scientific Investigations Report 2023–5090, 16 p., https://doi.org/10.3133/sir20235090.","productDescription":"Report: iv, 16 p.; Data Release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-149676","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":501055,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115441.htm","linkFileType":{"id":5,"text":"html"}},{"id":421394,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91CSH0P","text":"USGS data release","linkHelpText":"Data for characterizing changes in the 1-percent annual exceedance probability streamflows for climate change scenarios in the Housatonic River watershed—Massachusetts, Connecticut, and New York"},{"id":421390,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5090/coverthb.jpg"},{"id":421391,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5090/sir20235090.pdf","text":"Report","size":"8.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5090"},{"id":421392,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5090/sir20235090.XML"},{"id":421393,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5090/images"},{"id":421395,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235090/full"}],"country":"United States","state":"Connecticut, Massachusetts, New York","otherGeospatial":"Housatonic River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.73443369971218,\n 41.34286607604693\n ],\n [\n -72.73443369971218,\n 42.92331687334064\n ],\n [\n -74.64605479346228,\n 42.92331687334064\n ],\n [\n -74.64605479346228,\n 41.34286607604693\n ],\n [\n -72.73443369971218,\n 41.34286607604693\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
The San Francisco Bay supports thousands of breeding waterbirds annually and hosts large populations of American avocets (Recurvirostra americana), black-necked stilts (Himantopus mexicanus), and Forster’s terns (Sterna forsteri). These three species have relied largely on former commercial salt ponds in South San Francisco Bay, which provide wetland foraging habitat and island nesting habitat. The South Bay Salt Pond Restoration Project is in the process of restoring 50–90 percent of 15,100 acres of these former salt ponds to tidal marsh and tidal mudflats. Although this restoration is expected to have numerous benefits, including providing habitat for tidal wetland-dependent species, improving water quality, buffering against storm surge, and protecting inland areas from sea level rise, the reduction in former salt pond habitat and nesting islands may negatively affect breeding waterbirds. To address the reduction in former salt pond habitat available to waterbirds, the South Bay Salt Pond Restoration Project also includes enhancements to remaining pond habitat, such as the construction of new islands for nesting. Moreover, the South Bay Salt Pond Restoration Project follows an adaptive management plan in which waterbird response to the changing landscape is monitored over time to ensure that existing breeding waterbird populations are maintained. In this report, we provide results of waterbird nest monitoring in South San Francisco Bay during the 2022 breeding season and present these results in the context of annual nest monitoring in South San Francisco Bay since 2005. Overall, nest abundance in 2022 remained at or near 18-year lows for American avocets (176 nests) and black-necked stilts (97 nests), but Forster’s tern nest abundance (1,727 nests) was at an 18-year high, reversing historically low abundance observed during 2015–2017. In 2022, there were only 6 American avocet, 4 black-necked stilt, and 4 Forster’s tern major colony nesting sites, which is down from annual averages of 12.4, 6.6, and 6.6 observed during 2005–2009. Nest success (30 percent for American avocets, 29 percent for black-necked stilt, and 53 percent for Forster’s terns) was below the 2005–2007 baseline values established for the South Bay Salt Pond Restoration Project. Average egg-hatching success (98 percent, 100 percent, and 90 percent), and clutch sizes (3.68, 3.70, and 2.63 eggs) of American avocets, black-necked stilts, and Forster’s terns, respectively, were similar to values observed during 2005–2010. All three species displayed notable shifts in nest initiation dates in 2022, with American avocets and Forster’s terns nesting 10–11 days earlier and black-necked stilts nesting 10 days later than during 2005–2010. Finally, the enhanced, managed ponds with newly constructed islands (Ponds A16 and SF2) supported 86 percent of all the Forster’s tern nests recorded in South San Francisco Bay in 2022, which is the first time these managed ponds have hosted such a substantial number of tern nests.
","language":"English","publisher":"U.S. Geological Center","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231067","collaboration":"Prepared in cooperation with California State Coastal Conservancy, California Wildlife Foundation, California Department of Fish and Wildlife, U.S. Fish and Wildlife Service, and South Bay Salt Pond Restoration Project","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Ackerman, J.T., Hartman, C.A., and Herzog, M., 2023, Monitoring nesting waterbirds for the South Bay Salt Pond Restoration Project—2022 breeding season: U.S. Geological Survey Open-File Report 2023–1067, 25 p., https://doi.org/10.3133/ofr20231067.","productDescription":"vi, 25 p.","numberOfPages":"25","onlineOnly":"Y","ipdsId":"IP-151077","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":421402,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231067/full"},{"id":421401,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1067/images"},{"id":421400,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1067/ofr20231067.xml"},{"id":421398,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1067/covrthb.jpg"},{"id":421399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1067/ofr20231067.pdf","text":"Report","size":"9 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","otherGeospatial":"South Bay Salt Pond","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.11258522212694,\n 37.585319000832044\n ],\n [\n -122.11258522212694,\n 37.361346093418206\n ],\n [\n -121.87569252193163,\n 37.361346093418206\n ],\n [\n -121.87569252193163,\n 37.585319000832044\n ],\n [\n -122.11258522212694,\n 37.585319000832044\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The U.S. Geological Survey, in cooperation with the Oregon Department of Transportation (ODOT), evaluated the effects of cold-weather chloride deicers (road deicing chemicals) on groundwater quality, with a focus on chloride, near the Siskiyou Pass in southwestern Oregon. The study covered the period during July 2018 through February 2021. Between the years 2016 and 2020 ODOT applied up to 16,000 gallons per mile of chloride deicer and 143,000 pounds per mile of road salt along an 11-mile stretch of Interstate 5 (I-5) through the Siskiyou Pass. Despite the benefit of safer driving conditions, there are potentially negative environmental effects associated with the use of chloride-based deicers (such as magnesium chloride and sodium chloride). The results from this study are intended to help ODOT assess the water-quality effects from the application of chloride deicers at the Siskiyou Pass and inform decisions on how those chemicals are used.
Dissolved chloride concentrations tended to be greater in groundwater downgradient from I-5 compared to groundwater upgradient from the interstate. Specific conductance was a good predictor of dissolved chloride concentration (R2 = 0.905). Continuous monitoring showed that specific conductance measurements were greater at four downgradient spring-fed sites at the end of the study period compared with measurements at the beginning of the study. The study results indicate that chloride levels in shallow groundwater downgradient from I-5 are increasing, but dissolved chloride concentrations in domestic wells are not above the U.S. Environmental Protection Agency drinking water recommendations. The approach and methods used in this study, with modifications as site conditions warrant, can be applied in other areas of chloride deicer application to determine if groundwater is affected.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235107","collaboration":"Prepared in cooperation with Oregon Department of Transportation","usgsCitation":"Gingerich, S.B., Wise, D.R., and Stonewall, A.J., 2023, Assessing the effects of chloride deicer applications on groundwater near the Siskiyou Pass, southwestern Oregon, July 2018–February 2021 (ver. 1.1, July 2025): U.S. Geological Survey Scientific Investigations Report 2023–5107, 39 p., https://doi.org/10.3133/sir20235107.","productDescription":"Report: viii, 39 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-140151","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":492943,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2023/5107/versionHistory.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"Version history"},{"id":421408,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9D6XDIJ","text":"USGS data release","description":"USGS data release","linkHelpText":"Specific conductance and other groundwater quality data, Siskiyou Pass area, southwestern Oregon, 2018 to 2021"},{"id":421403,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5107/coverthb2.jpg"},{"id":421404,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5107/sir20235107.pdf","text":"Report","size":"7.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5107"},{"id":421405,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235107/full","text":"Report","description":"SIR 2023-5107"},{"id":421406,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5107/images"},{"id":421407,"rank":7,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5107/sir20235107.XML"},{"id":421409,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5107/sir20235107_appendix1.xlsx","text":"Appendix 1","size":"45 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2023-5107 Appendix 1"}],"country":"United States","state":"Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.79414603940278,\n 42.22368397834566\n ],\n [\n -122.79414603940278,\n 41.9878942723297\n ],\n [\n -122.42473672058753,\n 41.9878942723297\n ],\n [\n -122.42473672058753,\n 42.22368397834566\n ],\n [\n -122.79414603940278,\n 42.22368397834566\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: September 29, 2023; Version 1.1: July 25, 2025","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
This report describes a sensitivity analysis of a water-budget model that was completed to identify the most important types of hydrologic information needed to reduce the uncertainty of model recharge estimates. The sensitivity of model recharge estimates for the Hawaiian Islands of Oʻahu and Maui was analyzed for seven model parameters potentially affected by land-cover changes within a watershed. The seven model parameters tested were canopy capacity, canopy-cover fraction, crop coefficient, fog-catch efficiency, root depth, stemflow, and trunk-storage capacity.
Results of the sensitivity analysis were used to (1) quantify the relative importance of the seven model parameters to recharge assessments for three moisture zones (dry, mesic, and wet) on Oʻahu and Maui and (2) prepare a list of critical information needs for each moisture zone. The list of critical information needs was developed for three general types of land cover (forest, shrubland, and grassland) that are assumed to be affected by watershed management in the Hawaiian Islands. Identified information needs included estimates or measurements of (1) evapotranspiration processes needed to determine crop coefficients for land-cover types in all moisture zones, (2) rooting depths for land-cover types in the dry and mesic moisture zones, (3) canopy-cover fraction for forests in the wet and mesic moisture zones, (4) ratios of fog interception to rainfall for forests and shrublands in the wet moisture zone, and (5) canopy capacity for forests in the wet and mesic moisture zones. The list of information needs can guide data-collection strategies of future projects. Collection and analysis of the identified hydrologic information may help model users develop a better parameterization scheme, reduce uncertainty of values that model users assign to land-cover dependent parameters, and therefore allow future applications of the water-budget model to more accurately quantify how recharge in the Hawaiian Islands might be affected by future land-cover changes within a watershed.
","language":"English","publisher":"U.S. Geological Center","publisherLocation":"Reston, VA","doi":"10.3133/sir20235022","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management","usgsCitation":"Johnson, A.G., Mair, A., and Oki, D.S., 2023, Identifying the relative importance of water-budget information needed to quantify how land-cover change affects recharge, Hawaiian Islands: U.S. Geological Survey Scientific Investigations Report 2023–5022, 28 p., https://doi.org/10.3133/sir20235022.","productDescription":"Report: vi, 28 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-129378","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":500874,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115438.htm","text":"Maui","linkFileType":{"id":5,"text":"html"}},{"id":500873,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115437.htm","text":"Oahu","linkFileType":{"id":5,"text":"html"}},{"id":421316,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9X9ZEE3","text":"USGS Data Release","description":"Johnson, A.G., and Kāne, H.L., 2023, Model subareas and moisture zones used in a sensitivity analysis of a water-budget model completed in 2022 for the islands of Oahu and Maui, Hawaii: U.S. Geological Survey data release, https://doi.org/10.5066/P9X9ZEE3.","linkHelpText":"Model subareas and moisture zones used in a sensitivity analysis of a water-budget model completed in 2022 for the islands of Oahu and Maui, Hawaii"},{"id":421315,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5022/sir20235022.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":421314,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5022/covrthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Maui, O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.75858532451014,\n 21.145379373074235\n ],\n [\n -156.75858532451014,\n 20.508739201099033\n ],\n [\n -155.89066540263516,\n 20.508739201099033\n ],\n [\n -155.89066540263516,\n 21.145379373074235\n ],\n [\n -156.75858532451014,\n 21.145379373074235\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -158.3516029026351,\n 21.83029675022702\n ],\n [\n -158.3516029026351,\n 21.17099365241016\n ],\n [\n -157.59354626201016,\n 21.17099365241016\n ],\n [\n -157.59354626201016,\n 21.83029675022702\n ],\n [\n -158.3516029026351,\n 21.83029675022702\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Most of the population of the Treasure Valley and the surrounding area of southwestern Idaho and easternmost Oregon depends on groundwater for domestic supply, either from domestic or municipal-supply wells. Current and projected rapid population growth in the area has caused concern about the long-term sustainability of the groundwater resource. In 2016, the U.S. Geological Survey, in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources, began a project to construct a numerical groundwater-flow model of the westernmost portion of the western Snake River Plain aquifer system, called the Treasure Valley.
The development of the model was guided by several objectives, including:
The model was constructed with a spatial scale and level of detail that aimed to meet these objectives while balancing the sometimes-competing goals of fast runtimes, numerical stability, usability, and parsimony.
The Treasure Valley Groundwater Flow Model (TVGWFM) is a three-dimensional finite-difference numerical model constructed using MODFLOW 6 (Langevin and others, 2017, Documentation for the MODFLOW 6 Groundwater Flow Model: U.S. Geological Survey Techniques and Methods, book 6, chap. A55, 197 p., https://doi.org/10.3133/tm6A55). The model covers the westernmost portion of the western Snake River Plain and is discretized into a regular grid of 64 by 65 cells with a side length of 1 mile and 6 layers of varying depth and active area. A historical model period was developed consisting of 360 month-long stress periods for 1986–2015. The model builds upon previous modeling efforts by adding a transient period, incorporating new head and discharge observations to constrain parameters, incorporating information from the hydrogeologic framework model (HFM) of Bartolino (2019, Hydrogeologic framework of the Treasure Valley and surrounding area, Idaho and Oregon: U.S. Geological Survey Scientific Investigations Report 2019–5138, https://doi.org/10.3133/sir20195138) and incorporating refined estimates of evapotranspiration and irrigation classification of lands in the study area.
The TVGWFM includes all significant components of recharge to and discharge from the aquifer. Inflows include canal seepage, irrigation and precipitation recharge, mountain-front recharge, rivers and stream seepage, and seepage from Lake Lowell. Outflows include discharge to agricultural drainage ditches, discharge to rivers and streams, pumping, and discharge to Lake Lowell. Each recharge or discharge component is represented separately using individual MODFLOW 6 packages.
Parameter values were derived with a combination of trial-and-error steps and automated parameter estimation using PEST software (Doherty, J.E., 2005, PEST, model-independent parameter estimation–User manual: Watermark Numerical Computing, https://pesthomepage.org/documentation). Parameter estimates were constrained with several types of observation data, including water levels, water level changes, vertical water level differences, drain discharges, change in drain discharges, river seepage, seepage from Lake Lowell, and change in seepage from Lake Lowell. Material properties from the hydrogeologic framework were also used to assign the minimum and maximum values of some parameters.
A final parameter realization was reached that minimized residuals between the observed and modelled values for the various observation groups. Mean residuals for the observation groups were 15.4 feet (ft) for water levels, 0.2 ft for water level changes, 19.4 ft for vertical water level differences, −3.9 cubic feet per second (ft3/s) for drain discharges, 0.0 ft3/s for changes in drain discharge, 45.0 ft3/s for river seepage, −40.1 ft3/s for Lake Lowell seepage, and 126.3 ft3/s for changes in Lake Lowell seepage. The quality of the model’s fit to observations varied spatially, with notable areas of under- or over-simulation of water levels present to the northwest and southwest of Lake Lowell, in the foothills along the eastern model boundary, and near the City of Eagle. Trends were observed in the residuals of many of the observation groups, indicating that the model is missing or not fully reproducing some phenomena that are observed in the system.
The TVGWFM can be used as a tool for water resource planning, for understanding the interactions of groundwater and surface water at a basin scale, and for facilitating conjunctive management, but may lack the precision needed for water rights administration at a local scale. Additional sources of uncertainty or limitations of the model are noted. The quantity and spatial distribution of canal seepage and infiltration of irrigation water recharge, the largest sources of recharge to the system, are unknown and approximated indirectly. There is poor understanding of how canal seepage and incidental recharge change as land is converted from agricultural (irrigated) to suburban (semi-irrigated). These uncertainties will affect any scenarios that investigate changes to land use or irrigation practices. Finally, the model has relatively high water-level residuals around and to the southwest of Lake Lowell and should not be used to estimate water level effects in that region.
The model was built with multiple, broadly expressed objectives and did not optimize performance for specific uses. However, the model and the tools included in an associated data release provide ample flexibility to improve the model for future uses. Adjustments and improvements could be made by refining the model in an area of interest, collecting additional calibration data, applying more rigorous boundary conditions, or re-estimating model parameters to optimize model performance for a specific model forecast.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235096","collaboration":"Prepared in cooperation with the Idaho Water Resource Board and the Idaho Department of Water Resources","usgsCitation":"Hundt, S.A., and Bartolino, J.R., 2023, Groundwater-flow model of the Treasure Valley, southwestern Idaho, 1986–2015: U.S. Geological Survey Scientific Investigations Report 2023–5096, 107 p., https://doi.org/10.3133/sir20235096.","productDescription":"Report: xii, 107 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-127901","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":501062,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115439.htm","linkFileType":{"id":5,"text":"html"}},{"id":421318,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5096/sir20235096.pdf","text":"Report","size":"30.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5096"},{"id":421321,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5096/images"},{"id":421317,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5096/coverthb.jpg"},{"id":421320,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9U6OOPH","text":"USGS data release","description":"USGS data release","linkHelpText":"Data and archive for a groundwater flow model of the Treasure Valley aquifer system, southwestern Idaho"},{"id":421322,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5096/sir20235096.XML"}],"country":"United States","state":"Idaho","otherGeospatial":"Treasure Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.26392993194762,\n 44.27650517719664\n ],\n [\n -117.26392993194762,\n 42.71456173603502\n ],\n [\n -115.50611743194747,\n 42.71456173603502\n ],\n [\n -115.50611743194747,\n 44.27650517719664\n ],\n [\n -117.26392993194762,\n 44.27650517719664\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The Sparta-Memphis aquifer, present across much of eastern Arkansas, is the second most used groundwater resource in the State, with the Mississippi River Valley alluvial aquifer being the primary groundwater resource. The U.S. Geological Survey, in cooperation with Arkansas Department of Agriculture-Natural Resources Division, Arkansas Geological Survey, Natural Resources Conservation Service, Union County Water Conservation Board, and the Union County Conservation District, collects groundwater data across the Sparta-Memphis aquifer extent in Arkansas. This report presents water-level data for measurements conducted during two time periods, January–May 2013 and January–June 2015, and discusses water-level altitude changes for the 2011–13 and 2013–15 periods in the Sparta-Memphis aquifer. Accompanying water-level data in this report include groundwater-quality data for the period 2010–15 in the Sparta-Memphis aquifer. Groundwater data can guide ongoing and future groundwater-monitoring efforts and inform management of the aquifers in Arkansas.
Water levels measured at 306 wells from January to May 2013 and 273 wells from January to June 2015 are graphically presented as potentiometric-surface maps. Measurements from 2011, 2013, and 2015 were used in the construction of 2011–13 and 2013–15 water-level change maps. Select long-term hydrographs are included in the report to illustrate water-level changes at the local scale.
Water-level data show the influence of climate, pumping, and conservation and management efforts on groundwater levels. With respect to climate, the study area experienced extreme drought conditions between January 2011 and December 2012. The proximate effects of drought—increased evapotranspiration, decreased recharge, and increased irrigation needs—resulted in water-level declines that were particularly notable in the northern and central portions of the study area.
Groundwater sampled in 2010–15 from 148 wells completed in the Sparta-Memphis aquifer was analyzed for specific conductance, pH, chloride (Cl) concentration, and bromide (Br) concentration. In 2015, groundwater-quality data from 103 wells completed in the Sparta-Memphis aquifer had a median specific conductance of 356 microsiemens per centimeter at 25 degrees Celsius and a median Cl concentration of 9.5 milligrams per liter (mg/L). The data show two areas of higher Cl (greater than 10 mg/L) and higher Br (greater than 0.5 mg/L) concentrations in Union, Calhoun, and Bradley Counties in southern Arkansas and Monroe and Phillips Counties in eastern-central Arkansas. A Cl and Br mixing model indicates the two regions of wells may have different sources of higher salinity. In the greater Union County area, water in most wells may be a mixture of recharge or precipitation and higher salinity groundwater from the Nacatoch aquifer. Water in wells in eastern-central Arkansas may be sourced from aquifers having a higher Cl concentration (and thus, also a higher Cl-to-Br ratio).
Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Removal of invasive Silver Carp Hypophthalmichthys molitrix is a primary control action in North America. Strong avoidance responses to underwater sound and electricity have been shown to facilitate herding and mass removal of these fish. We conducted a telemetry study on a closed population of Silver Carp (i.e., 10 telemetered fish) to assess fine-scale movement responses to herding stimuli. Two herding boats traveled along bank-to-bank transects through the study area (longitudinal progression rate = 0.37 m/s) emitting sound and electricity (“combination technique”) or no added stimuli (“control”). The combination technique was most effective in terms of increasing fish presence (2.2 x the control) in the refuge-zones when herding had concluded and effective range (i.e., fish reaction distance; 1.6 x the control) relative to the herding boats. Fish median (~1 m/s) and maximum (~2 m/s) swimming velocity was relatively stable across fixed effects, except for the negative influence of water depth on maximum velocity. Water depth also exhibited a negative effect on fish reaction distance. Our results suggest effective range of the combination technique was conservatively 200 m (~20 dB re 1 μPa > ambient level) when accounting for water depth in the study area. Herding deployments less than 1 m/s (longitudinal progression) could control fish passing and maintain fish movements towards an intended location. Information provided herein can serve to assist planning, design, and application of herding efforts used to manage, control, and remove these invasive fish.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10955","usgsCitation":"Ridgway, J.L., Acre, M.R., Hessler, T.M., Broaddus, D., Morris, J., and Calfee, R.D., 2023, Silver carp herding: A telemetry evaluation of efficacy and implications for design and application: North American Journal of Fisheries Management, v. 43, no. 6, p. 1750-1764, https://doi.org/10.1002/nafm.10955.","productDescription":"15 p.","startPage":"1750","endPage":"1764","ipdsId":"IP-144362","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":442012,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10955","text":"Publisher Index Page"},{"id":435166,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VQECFP","text":"USGS data release","linkHelpText":"Telemetry evaluation of invasive carp herding in Jonathan Creek Embayment, Kentucky Lake, Kentucky"},{"id":422098,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kentucky","otherGeospatial":"Jonathan Creek embayment, Kentucky Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.19285937344272,\n 36.82937300194537\n ],\n [\n -88.22829442891907,\n 36.81640599482829\n ],\n [\n -88.24424020388349,\n 36.764718425521735\n ],\n [\n -88.21310797657148,\n 36.76046024751608\n ],\n [\n -88.19285937344272,\n 36.82937300194537\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"43","issue":"6","noUsgsAuthors":false,"publicationDate":"2023-09-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ridgway, Josey Lee 0000-0003-4157-7255","orcid":"https://orcid.org/0000-0003-4157-7255","contributorId":238277,"corporation":false,"usgs":true,"family":"Ridgway","given":"Josey","email":"","middleInitial":"Lee","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":886803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Acre, Matthew Ross 0000-0002-5417-9523","orcid":"https://orcid.org/0000-0002-5417-9523","contributorId":268034,"corporation":false,"usgs":true,"family":"Acre","given":"Matthew","email":"","middleInitial":"Ross","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":886804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hessler, Tyler Michael 0000-0001-5062-2340","orcid":"https://orcid.org/0000-0001-5062-2340","contributorId":272075,"corporation":false,"usgs":true,"family":"Hessler","given":"Tyler","email":"","middleInitial":"Michael","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":886805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Broaddus, Dustin 0000-0002-3160-0477","orcid":"https://orcid.org/0000-0002-3160-0477","contributorId":331134,"corporation":false,"usgs":true,"family":"Broaddus","given":"Dustin","email":"","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":886806,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morris, Jessica","contributorId":331135,"corporation":false,"usgs":false,"family":"Morris","given":"Jessica","email":"","affiliations":[{"id":53972,"text":"Kentucky Department of Fish and Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":886807,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Calfee, Robin D. 0000-0001-6056-7023 rcalfee@usgs.gov","orcid":"https://orcid.org/0000-0001-6056-7023","contributorId":1841,"corporation":false,"usgs":true,"family":"Calfee","given":"Robin","email":"rcalfee@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":886808,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70248976,"text":"sim3509 - 2023 - Groundwater potentiometric-surface altitude in 2022 and groundwater-level changes between 1968, 1991, and 2022, in the alluvial aquifer in the Big Lost River Valley, south-central Idaho","interactions":[],"lastModifiedDate":"2026-02-23T18:09:25.383109","indexId":"sim3509","displayToPublicDate":"2023-09-27T12:02:38","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3509","displayTitle":"Groundwater Potentiometric-Surface Altitude in 2022 and Groundwater-Level Changes Between 1968, 1991, and 2022, in the Alluvial Aquifer in the Big Lost River Valley, South-Central Idaho","title":"Groundwater potentiometric-surface altitude in 2022 and groundwater-level changes between 1968, 1991, and 2022, in the alluvial aquifer in the Big Lost River Valley, south-central Idaho","docAbstract":"The U.S. Geological Survey and the Idaho Department of Water Resources measured groundwater levels during spring 2022 and autumn 2022 to create detailed potentiometric-surface maps for the alluvial aquifer in the Big Lost River Valley in south-central Idaho. Wells were assigned to shallow, intermediate, and deep water-bearing units based on well depth, groundwater potentiometric-surface altitude, and hydrogeologic unit. Potentiometric-surface contours were created for each of the three water-bearing units for spring 2022 and autumn 2022. Groundwater flow generally follows topography down valley to the south. The groundwater-level data also were used to calculate changes in groundwater levels from spring to autumn 2022 and from historical measurement events in 1968 and 1991 to 2022. Groundwater levels declined at most wells from spring 1968 to spring 2022 and from spring 1991 to spring 2022. Although groundwater-level changes are sensitive to interannual wet and dry periods, long-term groundwater-level declines suggest that recharge and down-valley groundwater flows are insufficient to fully recover groundwater-level declines from pumping in some parts of the alluvial aquifer in the Big Lost River Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3509","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Ducar, S.D., and Zinsser, L.M., 2023, Groundwater potentiometric-surface altitude in 2022 and groundwater-level changes between 1968, 1991, and 2022, in the alluvial aquifer in the Big Lost River Valley, south-central Idaho: U.S. Geological Survey Scientific Investigations Map 3509, 1 sheet, scale 1:150,000, 11-p. pamphlet, https://doi.org/10.3133/sim3509.","productDescription":"Pamphlet: viii, 11 p.; Map: 22.51 × 30.00 inches","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-140355","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":500438,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115436.htm","linkFileType":{"id":5,"text":"html"}},{"id":421346,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93NQAP9","text":"USGS data release","description":"USGS data release","linkHelpText":"Groundwater potentiometric-surface contours and well numbers used to map groundwater potentiometric-surface altitude in 2022 and groundwater-level changes between 1968, 1991, and 2022 in the alluvial aquifer in the Big Lost River Valley, south-central Idaho"},{"id":421275,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3509/sim3509_pamphlet.XML"},{"id":421274,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3509/images"},{"id":421270,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3509/coverthb.jpg"},{"id":421271,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3509/sim3509.pdf","text":"Sheet","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3509"},{"id":421272,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/sim/3509/sim3509_pamphlet.pdf","text":"Pamphlet","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3509 Pamphlet"},{"id":421273,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3509/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"},"description":"SIM 3509 Pamphlet"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Lost River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.0,\n 44.15\n ],\n [\n -114,\n 43.30\n ],\n [\n -113.15,\n 43.3\n ],\n [\n -113.15,\n 44.15\n ],\n [\n -114,\n 44.15\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
In 2021, the U.S. Geological Survey (USGS), in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, studied floods to produce a library of flood-inundation maps for the Sabinal River near Utopia, Texas. Digital flood-inundation maps were created for a 10-mile reach of the Sabinal River from USGS streamgage 08197936 Sabinal River below Mill Creek near Vanderpool, Tex., at the upstream boundary of the study reach, to USGS streamgage 08197970 Sabinal River at Utopia, Tex. (hereinafter referred to as the “Utopia gage”), at the downstream boundary of the study reach, and for a 7-mile reach of the West Sabinal River. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to selected gage heights (the water-surface elevation at a streamgage, commonly referred to as “stage”) at the Utopia gage. Water-surface elevations were computed for the stream reach by means of a two-dimensional unsteady-state diffusion wave model with the U.S. Army Corps of Engineers Hydrologic Engineering Center River Analysis System program. A synthetic stage-discharge rating curve at the Utopia gage was developed using a regional regression equation to construct the model boundary condition inputs, and the upper bound of the stage-discharge relation was matched to a major flood event in July 2002. The hydraulic model was used to compute water-surface elevations for 35 stages at 0.5-foot (ft) increments referenced to the Utopia gage datum and ranging from 11 ft (near bankfull) to 28 ft (estimated peak stage during the July 2002 flood event). These flood-inundation maps, in conjunction with the real-time stage data from the Utopia gage, are intended to help guide the public in taking individual safety precautions and provide emergency management personnel with a tool to efficiently manage emergency flood operations and postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235001","issn":"2328-0328 (online)","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., 2023, Flood-inundation maps created using a synthetic rating curve for a 10-mile reach of the Sabinal River and a 7-mile reach of the West Sabinal River near Utopia, Texas, 2021 (ver. 2.0, September 2023): U.S. Geological Survey Scientific Investigations Report 2023–5001, 18 p., https://doi.org/10.3133/sir20235001.","productDescription":"Report: viii, 18 p.; Data Release","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-136311","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":435168,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CIK9ZF","text":"USGS data release","linkHelpText":"Geospatial and model dataset for flood-Inundation maps in a 10-mile reach of the Sabinal River and a 7-mile reach of the West Sabinal River near Utopia, Texas, 2021"},{"id":421129,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5001/coverthb.jpg"},{"id":500486,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114428.htm","linkFileType":{"id":5,"text":"html"}},{"id":421198,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/fs20233001","text":"Fact Sheet 2023–3001","description":"USGS Fact Sheet 2023–3001","linkHelpText":"- Flood Warning Toolset for the Sabinal River Near Utopia, Texas"},{"id":421197,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2023/5001/versionHist.txt","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2023-5001 version history"},{"id":421196,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5001/sir20235001.XML","linkFileType":{"id":8,"text":"xml"},"description":"SIR 2023-5001 XML"},{"id":421193,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5001/images"},{"id":421599,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235001/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5001 HTML"},{"id":421194,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5001/sir20235001.pdf","text":"Report","size":"2.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5001"}],"country":"United States","state":"Texas","city":"Utopia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.38878556700459,\n 29.515266260991964\n ],\n [\n -99.38878556700459,\n 29.797981198043047\n ],\n [\n -99.67156342604174,\n 29.797981198043047\n ],\n [\n -99.67156342604174,\n 29.515266260991964\n ],\n [\n -99.38878556700459,\n 29.515266260991964\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: February 2023; Version 2.0: September 2023","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The Equus Beds aquifer and Cheney Reservoir are primary sources for the city of Wichita’s current (2023) water supply. The Equus Beds aquifer storage and recovery (ASR) project was developed by the city of Wichita in the early 1990s to meet future water demands using the Little Arkansas River as an artificial aquifer recharge water source during above-base-flow conditions. Little Arkansas River water is removed from the river at an ASR Facility intake structure, treated using National Primary Drinking Water Regulations as a guideline, and is infiltrated into the Equus Beds aquifer through recharge basins or injected into the aquifer through recharge wells for later use. The U.S. Geological Survey, in cooperation with the city of Wichita, completed this study to quantify and characterize Little Arkansas River water-quality data. Data in this report can be used to evaluate changing conditions, provide science-based information for decision making, and help meet regulatory requirements.
Continuous (hourly) physicochemical properties were measured, and discrete water-quality samples were collected from three Little Arkansas River sites located along the easternmost extent of the Equus Beds aquifer during 1995 through 2021 over a range of streamflow conditions. The Little Arkansas River at Highway 50 near Halstead, Kansas, streamgage (U.S. Geological Survey station 07143672; hereafter referred to as the “Highway 50 site”) is located upstream from the other two sites, and the Little Arkansas River near Sedgwick, Kans., streamgage (U.S. Geological Survey station 07144100; hereafter referred to as the “Sedgwick site”) is located downstream from the other two sites; these two sites bracket most of the easternmost part of the Equus Beds aquifer. The Little Arkansas River upstream of ASR Facility near Sedgwick, Kans., streamgage (U.S. Geological Survey station 375350097262800; hereafter referred to as the “Upstream ASR site”) is located between the Highway 50 and Sedgwick sites, about 14.7 river miles (mi) downstream from the Highway 50 site, about 1.7 river mi upstream from the Sedgwick site, and immediately upstream from the ASR Facility intake structure. Surrogate models for water-quality constituents of interest (including bromide, dissolved organic carbon, 2-chloro-4-isopropylamino-6-amino-s-triazine [deethylatrazine], atrazine, and metolachlor) were updated or developed using continuously measured and concomitant discrete data. These surrogate models, along with previously developed regression models, were used to compute concentrations (at the Highway 50, Sedgwick, and Upstream ASR sites) and loads (at the Highway 50 and Sedgwick sites) during the study period. Federal criteria were used to evaluate water quality. Where applicable, water-quality data were compared to Federal national drinking-water regulations. Flow-normalized water-quality constituent trends were evaluated using Weighted Regressions on Time, Discharge, and Season (WRTDS) statistical models and water-quality trends were described using WRTDS bootstrap tests.
Continuously computed primary ion concentrations were generally larger at the Highway 50 site compared to the Sedgwick site. During the study period, the Federal secondary maximum contaminant level (SMCL) for dissolved solids was exceeded 57 percent of the time at the Highway 50 site and 38 percent of the time at the Sedgwick site. Computed bromide concentrations were larger at the Highway 50 site and exceeded the city of Wichita treatment threshold about 70, 21, and 19 percent of the time at the Highway 50, Sedgwick, and Upstream ASR sites, respectively. Chloride concentrations exceeded the Federal SMCL about 16 percent of the time at the Highway 50 site and did not exceed the SMCL at the Sedgwick site. Continuous arsenic concentrations exceeded the Federal Maximum Contaminant Level (MCL) 9 to 15 percent of the time at the Sedgwick and Highway 50 sites, respectively, during the study. Atrazine concentrations exceeded the Federal MCL 10 percent of the time at the Highway 50 and Sedgwick sites and 14 percent of the time at the Upstream ASR site during the study; computed glyphosate concentrations at the Sedgwick site never exceeded the MCL during the study.
Little Arkansas River flow-normalized primary ion concentrations during 1995 through 2021 generally had downward trends and decreases were generally larger at the Highway 50 site compared to the Sedgwick site. Dissolved solids and chloride concentrations decreased at the Highway 50 and Sedgwick sites. Bromide had no trend at the Highway 50 site and a downward trend at the Sedgwick site. Nitrate plus nitrite and total phosphorus concentrations had upward trends at the Highway 50 site but downward trends at the Sedgwick site, whereas total organic carbon had upward trends at both sites. Nitrate plus nitrite, total nitrogen, total phosphorus, and total organic carbon fluxes had upward trends at the Highway 50 and Sedgwick sites. Suspended-sediment concentrations had an upward trend at the Highway 50 site and had no trend at the Sedgwick site. Arsenic concentrations had downward trends at the Highway 50 and Sedgwick sites.
About one-quarter to one-half of the Little Arkansas River loads, including nutrients and sediment, were transported during 1 percent of the time during the study. Because streamflows are highly sensitive to climatic variation and an increase of extreme precipitation events in the Great Plains is expected, similar disproportionately large pollutant loading events may increase into the future. Continuous measurement of physicochemical properties in near-real time allowed characterization of Little Arkansas River surface water during conditions and time scales that would not have been possible otherwise and served as a complement to discrete water-quality sampling. Continuation of this water-quality monitoring will provide data to characterize changing conditions in the Little Arkansas River and possibly identify new and changing trends. Information in this report allows the city of Wichita to make informed municipal water-supply decisions using past and present water-quality conditions and trends in the watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235102","collaboration":"Prepared in cooperation with the city of Wichita, Kansas","usgsCitation":"Stone, M.L., and Klager, B.J., 2023, Long-term water-quality constituent trends in the Little Arkansas River, south-central Kansas, 1995–2021: U.S. Geological Survey Scientific Investigations Report 2023–5102, 103 p., https://doi.org/10.3133/sir20235102.","productDescription":"Report: ix, 103 p.; 1 Figure; 9 Tables; 5 Appendixes; Dataset","numberOfPages":"118","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-146544","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":421187,"rank":26,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix10.zip","text":"Appendix 10","size":"46 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season Graphical Output at station 07144100"},{"id":421186,"rank":25,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix9.zip","text":"Appendix 9","size":"35 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season Graphical Output at station 07143672"},{"id":421177,"rank":24,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix6.zip","text":"Appendix 6","size":"2.6 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Surrogate Regression Model Archive Summaries for the Little Arkansas River upstream of ASR Facility near Sedgwick, Kansas"},{"id":421176,"rank":23,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix5.zip","text":"Appendix 5","size":"2.7 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Surrogate Regression Model Archive Summaries for the Little Arkansas River near Sedgwick, Kansas"},{"id":421175,"rank":22,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_appendix4.zip","text":"Appendix 4","size":"1.1 MB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Surrogate Regression Model Archive Summaries for the Little Arkansas River at Highway 50 near Halstead, Kansas"},{"id":421185,"rank":19,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table8.3.csv","text":"Table 8.3","size":"9 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season estimated mean, flow-normalized, and generalized mean fluxes for sediment, indicator bacteria, and trace elements at the Little Arkansas River at Highway 50 near Halstead, Kansas, and Little Arkansas River near Sedgwick, Kans., 1995–2021"},{"id":421184,"rank":18,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table8.2.csv","text":"Table 8.2","size":"10 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season estimated mean, flow-normalized, and generalized mean fluxes for nutrients and carbon species at the Little Arkansas River at Highway 50 near Halstead, Kansas, and Little Arkansas River near Sedgwick, Kans., 1995–2021"},{"id":421183,"rank":17,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table8.1.csv","text":"Table 8.1","size":"12 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season estimated mean, flow-normalized, and generalized mean fluxes for primary ions at the Little Arkansas River at Highway 50 near Halstead, Kansas, and Little Arkansas River near Sedgwick, Kans., 1995–2021"},{"id":421181,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table7.3.csv","text":"Table 7.3","size":"8 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season estimated mean, flow-normalized, and generalized mean concentrations or densities for sediment, indicator bacteria, and trace elements at the Little Arkansas River at Highway 50 near Halstead, Kansas, and Little Arkansas River near Sedgwick, Kans., 1995–2021"},{"id":421180,"rank":14,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table7.2.csv","text":"Table 7.2","size":"10 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season estimated mean, flow-normalized, and generalized mean concentrations for nutrients and carbon species at the Little Arkansas River at Highway 50 near Halstead, Kansas, and Little Arkansas River near Sedgwick, Kans., 1995–2021"},{"id":421179,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table7.1.csv","text":"Table 7.1","size":"12 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season estimated mean, flow-normalized, and generalized mean concentrations for primary ions at the Little Arkansas River at Highway 50 near Halstead, Kansas, and Little Arkansas River near Sedgwick, Kans., 1995–2021"},{"id":421178,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_tables7.1-7.3.xlsx","text":"Tables 7.1–7.3","size":"108 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":421174,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table3.1.csv","text":"Table 3.1","size":"6.3 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Highway 50 near Halstead, Kansas; Little Arkansas River near Sedgwick, Kans.; and Little Arkansas River upstream of ASR Facility near Sedgwick, Kans., 2004–19"},{"id":421170,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_fig1.1.PDF","text":"Figure 1.1","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Relations between turbidity sensors, 2004–19. A, YSI 6026 (YSI6026) and YSI 6136 (YSI6136) at the Little Arkansas River at Highway 50 near Halstead, Kansas"},{"id":421190,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":421169,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5102/images/"},{"id":421168,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102.XML","linkFileType":{"id":8,"text":"xml"}},{"id":501150,"rank":27,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115440.htm","linkFileType":{"id":5,"text":"html"}},{"id":421182,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_tables8.1-8.3.xlsx","text":"Tables 8.1–8.3","size":"112 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":421167,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102.pdf","text":"Report","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5102"},{"id":421188,"rank":20,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table11.1.xlsx","text":"Table 11.1","size":"51 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Weighted Regressions on Time, Discharge, and Season estimated yearly water-quality constituent loads at the Little Arkansas River at Highway 50 near Halstead, Kansas and near Sedgwick, Kans., 1998–2021"},{"id":421166,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5102/coverthb.jpg"},{"id":421189,"rank":21,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2023/5102/sir20235102_table11.1.csv","text":"Table 11.1","size":"14 KB","linkFileType":{"id":7,"text":"csv"}},{"id":421201,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235102/full","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -98.1667,\n 38.6\n ],\n [\n -98.1667,\n 37.5\n ],\n [\n -97.25,\n 37.5\n ],\n [\n -97.25,\n 38.6\n ],\n [\n -98.1667,\n 38.6\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
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Numerous studies have evaluated the application of Remote Sensing (RS) techniques for mapping actual evapotranspiration (ETa) using Vegetation-Index-based (VI-based) and surface energy balance methods (SEB). SEB models computationally require a large effort for application. VI-based methods are fast and easy to apply and could therefore potentially be applied at high resolution; however, the accuracy of VI-based methods in comparison to SEB-based models remains unclear. We tested the ETa computed with the modified 2-band Enhanced Vegetation Index (METEVI2) implemented in the Google Earth Engine – for mapping croplands’ water use dynamics in the Lower Colorado River Basin. We compared METEVI2 with the well-established RS-based products of OpenET (Ensemble, eeMETRIC, SSEBop, SIMS, PT_JPL, DisALEXI and geeSEBAL). METEVI2 was then evaluated with measured ETa from four wheat fields (2017–2018). Results indicated that the monthly ETa variations for METEVI2 and OpenET models were comparable, though of varying magnitudes. On average, METEVI2 had the lowest difference rate from the average observed ETa with 17 mm underestimation, while SIMS had the highest difference rate (82 mm). Findings show that METEVI2 is a cost-effective ETa mapping tool in drylands to track crop water use. Future studies should test METEVI2’s applicability to croplands in more humid regions.
Amphibians are among the most sensitive taxa to climate change, and species inhabiting arid and semiarid landscapes at the extremes of their range are especially vulnerable to drought. The Jack Creek, Oregon, USA, population of Oregon spotted frogs (Rana pretiosa) faces unique challenges because it occupies the highest elevation site in the species' extant range and one that has been transformed by loss of American beavers (Castor canadensis), which historically maintained open water. We evaluated the effects of drought mitigation (addition of excavated ponds) on relationships between local and regional water availability, inactive legacy beaver dams, and Oregon spotted frog population dynamics in the Jack Creek system. We conducted egg mass surveys and capture-mark-recapture sampling at a treatment reach with excavated ponds and 3 reference reaches over 13 years; surveys spanned a period before and after pond excavation at the treatment and 1 primary comparison reference reach. We analyzed data using a combination of robust design capture-mark-recapture estimators and generalized linear mixed models to characterize population dynamics. Adult Oregon spotted frog survival was approximately 19.5% higher at the treatment reach than the primary reference reach during the study period. Annual survival was most strongly associated with late summer vegetation greenness, a proxy for water availability, and males had higher survival than females. Among the 4 study reaches, the treatment reach consistently had higher late summer vegetation greenness, and the hydrology functioned more independently of regional precipitation patterns relative to the reference reaches; however, these dynamics were not linked to pond excavation. Breeding was concentrated in 2 legacy beaver ponds that were deepened by excavation during the study compared to an unexcavated beaver pond, 2 excavated ponds without legacy beaver dams, and 9 reference ponds. These results point to the benefit of enhancing existing beaver structures and indicate that management actions aimed at maintaining surface water for breeding in spring and saturated soils and ponded water for adults in late summer would benefit this unique population of Oregon spotted frogs in the face of drought.
The Indio subbasin of the Coachella Valley is a desert area of southern California where a growing population depends primarily on groundwater for drinking and agricultural uses. The aquifer system has been supplemented with Colorado River water through managed recharge and widespread irrigation since the mid-20th century. We use a combination of geochemical modeling and trend analysis to identify changes in total dissolved solids through time, elucidate the sources of dissolved solids, and quantify the extent of contributions from those sources throughout the Indio subbasin. We conclude that recharged Colorado River water is the primary source and driver of increasing salinity, particularly in areas immediately downgradient from the recharge locations and in the eastern part of the subbasin away from the recharge ponds due to irrigation using imported water. Other contributions of dissolved solids to groundwater resources include geothermal waters, wastewater effluent, and agricultural return flow, although their effects are more localized. This study presents a broadly applicable framework for identifying sources of dissolved solids in groundwater wells and salinity trends at a regional scale in a large data set.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acsestwater.3c00239","usgsCitation":"Harkness, J.S., McCarthy, P.M., Jurgens, B., and Levy, Z., 2023, Salinity trends in a groundwater system supplemented by 50 years of imported Colorado River water: Environmental Science & Technology Water, v. 3, no. 10, p. 3253-3264, https://doi.org/10.1021/acsestwater.3c00239.","productDescription":"12 p.","startPage":"3253","endPage":"3264","ipdsId":"IP-148329","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":442051,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acsestwater.3c00239","text":"Publisher Index Page"},{"id":435173,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KUBQKM","text":"USGS data release","linkHelpText":"Inverse Model Data for: Salinity trends in a groundwater system supplemented by 50 years of imported Colorado River water"},{"id":421073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Coachella Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.13573439891272,\n 33.44388210065128\n ],\n [\n -115.83959902010358,\n 33.47539980820645\n ],\n [\n -116.10770512437531,\n 33.77373918192059\n ],\n [\n -116.49280298323828,\n 33.977110302145775\n ],\n [\n -116.5817654632921,\n 33.996309353196736\n ],\n [\n -116.645135997029,\n 33.92049840312886\n ],\n [\n -116.51108294489313,\n 33.79298404948595\n ],\n [\n -116.29781672558602,\n 33.66122225831866\n ],\n [\n -116.21372890197347,\n 33.55363612855925\n ],\n [\n -116.13573439891272,\n 33.44388210065128\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"3","issue":"10","noUsgsAuthors":false,"publicationDate":"2023-09-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Harkness, Jennifer S. 0000-0001-9050-2570 jharkness@usgs.gov","orcid":"https://orcid.org/0000-0001-9050-2570","contributorId":224299,"corporation":false,"usgs":true,"family":"Harkness","given":"Jennifer","email":"jharkness@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883884,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarthy, Patrick Michael 0000-0002-5492-1409","orcid":"https://orcid.org/0000-0002-5492-1409","contributorId":330033,"corporation":false,"usgs":true,"family":"McCarthy","given":"Patrick","email":"","middleInitial":"Michael","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883885,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203409,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Levy, Zeno F. 0000-0003-4580-2309","orcid":"https://orcid.org/0000-0003-4580-2309","contributorId":222340,"corporation":false,"usgs":true,"family":"Levy","given":"Zeno","middleInitial":"F.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":883887,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248564,"text":"fs20233039 - 2023 - Spatial distribution of elevation change monitoring in coastal wetlands across protected lands of the lower 48 United States","interactions":[],"lastModifiedDate":"2026-02-09T17:44:36.631457","indexId":"fs20233039","displayToPublicDate":"2023-09-20T14:30:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3039","displayTitle":"Spatial Distribution of Elevation Change Monitoring in Coastal Wetlands Across Protected Lands of the Lower 48 United States","title":"Spatial distribution of elevation change monitoring in coastal wetlands across protected lands of the lower 48 United States","docAbstract":"Tidally influenced coastal wetlands, both saline and fresh, appear where terrestrial and marine environments meet and are considered important ecosystems for identifying the impacts of climate change. Coastal wetlands provide valuable benefits to society and the environment in the form of flood protection, water-quality improvements, and shoreline erosion reduction, making them one of the most important ecosystems in the world. Historically, these ecosystems have vertically adjusted to match rising sea levels through biologic and physical processes, but they are increasingly vulnerable to submergence as sea-level rise accelerates. Measuring vertical change on lands protected from human influence allows scientists to understand how vulnerable coastal wetlands are to submergence. But to fully understand this vulnerability, scientists must identify where vertical change in coastal wetlands is being measured across the lower 48 United States, a task that has not yet been undertaken. 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U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
The soil and groundwater at the Central Chemical facility, Hagerstown, Maryland, are contaminated due to the blending and production of pesticides and fertilizers during much of the 20th century. Remedial investigations focus on two operable units (OU) consisting of the surface soils and waste disposal lagoon (OU-1) and the groundwater (OU-2). The contaminants of concern (COC) for groundwater include 41 compounds categorized within the subgroups of volatile organic compounds (VOCs), semi-volatile organic compounds (SVOCs), pesticides, and metals. The purpose of this report is to provide a conceptual site model of the hydrogeology and groundwater contaminant transport at and near the Central Chemical facility. The conceptual model was developed through review, synthesis, and interpretation of the results of hydrogeologic, soil, and other environmental investigations conducted at and in the vicinity of the facility in recent decades and is intended to support plans for environmental remediation of the groundwater in OU-2.
The extent and nature of the groundwater contaminant plume associated with the bedrock was characterized for OU-2 of the site. Lithologic and structural comparisons between shallow soil, weathered rock, and epikarst and deeper competent but bedded, dipping, fractured, and karstic limestones illustrate two connected flow systems—a surficial flow system consisting of the unconsolidated overburden and epikarst and a structurally dominant bedrock flow system below the epikarst. Uncertainties exist regarding the nature and transport of contaminants within the epikarst system particularly within voids and perched epikarst water tables. Karst dissolution features are observed within the site including sinkholes and dissolution voids within wells at the site. Of interest, one well in the northern part of the study area (MW-J-71) appears to have a dissolution void connected to an offsite well (OW-2-115) farther to the north. This connection is supported by groundwater level data and elevated concentrations of total suspended solids (TSS) and chlorobenzene in both wells. The high level of TSS supports the possibility of offsite transport of particle-bound contaminants within the conduit system. Episodically elevated concentrations of COC from different groups also were observed within select wells in the epikarst near the waste disposal lagoon (particularly MW-A-51). The variability observed between different COC within the same well may be the result of additional contaminated source materials unrelated to the disposal lagoon. Storage and episodic transport of contaminated material within the epikarst system has the potential to hinder remediation efforts if not considered in the remedial action.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225011","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Needham, T.P., Fiore, A.R., Ator, S.W., Raffensperger, J.P., Smith, M.B., Bellmyer, N.M., Dugan, C.M., and Morel, C.J., 2023, Geology, hydrology, and groundwater contamination in the vicinity of Central Chemical facility, Hagerstown, Maryland: U.S. Geological Survey Scientific Investigations Report 2022–5011, 62 p., https://doi.org/10.3133/sir20225011.","productDescription":"ix, 62 p.","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127106","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":500691,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115410.htm","linkFileType":{"id":5,"text":"html"}},{"id":420978,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5011/images/"},{"id":420977,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5011/sir20225011.XML"},{"id":420976,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225011/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5011"},{"id":420975,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5011/sir20225011.pdf","text":"Report","size":"13.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5011"},{"id":420974,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5011/coverthb.jpg"}],"country":"United States","state":"Maryland","city":"Hagerstown","otherGeospatial":"Central Chemical Facility","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.7208,\n 39.6542\n ],\n [\n -77.7208,\n 39.6597\n ],\n [\n -77.726,\n 39.6597\n ],\n [\n -77.726,\n 39.6542\n ],\n [\n -77.7208,\n 39.6542\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228
Per- and polyfluoroalkyl substances (PFAS) are a group of synthetic chemicals that have been manufactured and used globally since the 1940s. PFAS are used for their oil- and water-repellent properties, ability to reduce friction, and their flame-retardant nature. PFAS are widely used in a variety of products, including clothing, carpet, food packaging, and firefighting foam. The properties that make them useful in manufacturing, however, also make them persistent and mobile, causing potential exposures to the environment and humans. Known as “forever chemicals,” these compounds resist degradation and have been determined to bioaccumulate in humans and wildlife.
The Illinois Environmental Protection Agency (IEPA) collected a total of 1,711 samples (includes quality-control samples) of finished water at 1,428 entry points from 1,017 Illinois community water supply (CWS) systems and analyzed the water samples for PFAS. The results following confirmation samples indicated a mean of 99 percent of all sample results were below the minimum reporting level (MRL) of 2 nanograms per liter (ng/L). Of the detections at or above the MRL, 7 of 18 PFAS were detected in 149 of 1,428 entry points (about 10 percent). Of the nearly 7.4 million residents directly served by the CWS systems sampled, more than 1.3 million residents (about 18 percent) are served by CWS systems that had at least one detection of PFAS above the MRL of 2 ng/L. The most frequently detected PFAS were perfluorobutanesulfonic acid (about 6.2 percent, 37 ng/L maximum concentration), perfluorooctanesulfonic acid (PFOS) (about 5.0 percent, 150 ng/L maximum concentration), and perfluorooctanoic acid (PFOA) (about 4.8 percent, 25 ng/L maximum concentration). Of the 1,428 entry point samples from the CWS systems, 149 samples had confirmed detections of PFAS, with 93 of those 149 (about 62 percent) samples having at least one PFAS with a concentration that exceeded the median detected concentration of 3.2 ng/L. The highest concentrations detected were 150 ng/L (PFOS) and 140 ng/L (perfluorohexanesulfonic acid) at one CWS location which has been shut down and a different source of water has been provided to the consumers.
Although PFAS detections were more common in CWS systems using surface-water sources (about 35 percent, 30 of 85) and mixed sources (50 percent, 5 of 10) compared to those using groundwater sources (about 9 percent, 114 of 1,333), a greater range of PFAS concentrations were observed in groundwater CWS systems (2 to 150 ng/L) than in surface-water CWS systems (2 to 15 ng/L). Statistically significant differences were determined between some detected PFAS (PFOA, PFOS, and perfluorohexanoic acid) and the source of drinking water (groundwater, surface water, or mixed).
This report summarizes the occurrence and spatial distribution of PFAS in CWS systems across Illinois. The results from this sampling effort could be used by Illinois public health officials to identify the potential risk of PFAS in drinking water to human health.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235078","programNote":"Prepared in cooperation with the Illinois Environmental Protection Agency","usgsCitation":"Gahala, A.M., Sharpe, J.B., and Williams, A.M., 2023, Statewide sampling to determine spatial distribution, prevalence, and occurrence of per- and polyfluoroalkyl substances (PFAS) in Illinois community water supplies, 2020–21: U.S. Geological Survey Scientific Investigations Report 2023–5078, 24 p., https://doi.org/10.3133/sir20235078.","productDescription":"Report: vii, 24 p.; 2 Datasets","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-137121","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":420947,"rank":7,"type":{"id":39,"text":"HTML 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The U.S. Geological Survey and collaborators from EcoAnalysts, Inc., completed field and laboratory studies during 2016–19 to evaluate the toxicity of metals to freshwater mussels in streams draining the Tri-State Mining District. This project consisted of (1) sampling and analysis of metals in water and sediment, (2) surveys of mussel assemblages at sites with suitable mussel habitat, (3) toxicity tests with juvenile mussels exposed to zinc or to a mixture of metals (zinc, lead, and cadmium) in water, and (4) toxicity tests to evaluate the contributions of metals in sediment and metals in overlying water to toxic effects on mussels. Field sampling at sites in the Spring River and Neosho River and their tributaries demonstrated wide ranges of metal contamination in water and sediment. Zinc was the predominant toxic metal in water, and concentrations of lead and cadmium were much lower. Mussel areal density and species richness were greater at reference sites with low sediment metal concentrations (for example, zinc, 29–141 micrograms per gram) than at test sites that had higher concentrations of sediment zinc (416–3,420 micrograms per gram) as a result of effects of upstream mining activity. Juvenile mussels were highly sensitive to zinc in water in 12-week toxicity tests compared to previous water-only tests, and adding low levels of waterborne lead and cadmium typical of their occurrence in Tri-State Mining District streams produced greater toxicity. Thresholds for mussel toxicity were at or less than waterborne metal concentrations detected in Tri-State Mining District streams, and sites with waterborne metal concentrations exceeding thresholds had decreased mussel density and decreased mussel species richness. The 12-week toxicity tests with juvenile mussels in Tri-State Mining District sediments also demonstrated negative mussel responses with metal exposure. Thresholds for reductions in survival, growth, or biomass were at sediment metal concentrations less than thresholds reported for previous 4-week tests. We documented strong associations between reduced survival in laboratory tests and reduced species richness in community surveys. Attempts to estimate combined toxicity thresholds for metals in sediment and overlying water were not successful. These inconclusive results may be attributable to several factors, including (1) unexpected losses of waterborne metals from solution, (2) differences in sensitivity of different age/size classes of juvenile mussels, (3) disruption of sediment-water equilibria and changes in metal bioavailability, and (4) behavioral or physiological responses allowing juvenile mussels to temporarily reduce or avoid metal exposure. We also observed differences in metal toxicity thresholds between sediment toxicity tests started with different ages/sizes of test organisms. A followup study that combined exposure to Tri-State Mining District sediments with exposures to multiple levels of waterborne metals demonstrated toxic effects of sediments with low metal concentrations; however, some treatments also indicated unexpected reversals of concentration-response trends and reduced toxicity in treatments that had high metal concentrations in overlying water. These unusual responses may reflect development of physiological tolerance to metal toxicity by induction of metal-binding proteins (for example, metallothionein) in response to high metal levels in water.
Results of laboratory and field studies indicated strong associations between metal exposure in Tri-State Mining District streams and toxic effects on juvenile freshwater mussels. Mussel community characteristics corresponded to differences in metal concentrations in sediment and water among Tri-State Mining District sampling sites. Responses of juvenile mussels in 12-week water and sediment exposures were strongly correlated with the status of mussel assemblages in Tri-State Mining District streams. The combined results support the hypothesis that exposure to metals from historical mining activities adversely affects freshwater mussel communities in the Spring River/Neosho River drainage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231024","usgsCitation":"Besser, J.M., Ivey, C.D., Kunz, J.L., Kemble, N.E., Cleveland, D.M., Steevens, J.A., Dunn, H., and Foley, R., 2023, Responses of juvenile mussels to metals in sediment and water of the Tri-State Mining District: U.S. Geological Survey Open-File Report 2023–1024, 80 p., https://doi.org/10.3133/ofr20231024.","productDescription":"Report: x, 80 p.; Data Release","numberOfPages":"94","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-144147","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":499773,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115408.htm","linkFileType":{"id":5,"text":"html"}},{"id":420914,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1024/ofr20231024.pdf","text":"Report","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1024"},{"id":420913,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1024/coverthb.jpg"},{"id":420915,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R9FHAF","text":"USGS data release","linkHelpText":"Response of juvenile mussels and amphipods to metal concentrations in water and sediment of streams draining the Tri-State Mining District, Missouri, Kansas and Oklahoma, USA"}],"country":"United States","state":"Kansas, Missouri, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95,\n 37.5\n ],\n [\n -95,\n 36.5\n ],\n [\n -94,\n 36.5\n ],\n [\n -94,\n 37.5\n ],\n [\n -95,\n 37.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Columbia Environmental Research Center
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