Wildlife reintroduction is an important conservation tool for threatened species, yet identifying appropriate source populations poses a challenge. In particular, the possibility of outbreeding depression is cited as a constraint limiting the range of candidate source populations for translocation. When multiple source lineages are mixed during reintroduction, genetic monitoring is necessary to evaluate whether sources contribute equally to subsequent generations and whether they are interbreeding as expected. Moreover, statistical analysis of genetic data should account for complex life histories that might affect the timescale of admixture and genetic drift. Here, we use samples collected over a 23-year period and a stochastic age-structured model to analyze the genetic mixing process in reintroduced Brook Trout (Salvelinus fontinalis) populations in the Southern Appalachians. Each restored population was seeded with two to three source populations. Previous research inferred reproductive isolation between source populations leading to a proposal of splitting the species into multiple taxa. In contrast, we found patterns of ancestry that were consistent with random mating and no advantage for one source lineage over any other. Brook Trout from different source streams are mixing as expected in the restoration sites. This result does not support the hypothesis that Brook Trout in the Southern Appalachian Mountains includes several distinct species. Mixing different sources from the same watershed seems to be an effective way to increase genetic diversity of reintroduced populations while minimizing risk to source populations.
","language":"English","publisher":"Springer","doi":"10.1007/s10592-024-01620-y","usgsCitation":"Smith, R.J., Kazyak, D.C., Kulp, M.A., Lubinski, B.A., and Fitzpatrick, B.M., 2024, Genetic structure of restored Brook Trout populations in the Southern Appalachian Mountains indicates successful reintroductions: Conservation Genetics, v. 25, p. 1007-1020, https://doi.org/10.1007/s10592-024-01620-y.","productDescription":"14 p.","startPage":"1007","endPage":"1020","ipdsId":"IP-158952","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":428350,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Tennessee","otherGeospatial":"Great Smoky Mountains National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.90629478474587,\n 35.85116809750147\n ],\n [\n -84.21639200734252,\n 35.85116809750147\n ],\n [\n -84.21639200734252,\n 35.2665417042201\n ],\n [\n -82.90629478474587,\n 35.2665417042201\n ],\n [\n -82.90629478474587,\n 35.85116809750147\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"25","noUsgsAuthors":false,"publicationDate":"2024-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Rebecca J.","contributorId":229064,"corporation":false,"usgs":false,"family":"Smith","given":"Rebecca","email":"","middleInitial":"J.","affiliations":[{"id":41574,"text":"National Park Service, Yellowstone National Park, PO Box 168, 22 Stable Street, Yellowstone National Park, WY, 82190, USA","active":true,"usgs":false}],"preferred":false,"id":900031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":900032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kulp, Matt A.","contributorId":196801,"corporation":false,"usgs":false,"family":"Kulp","given":"Matt","email":"","middleInitial":"A.","affiliations":[{"id":35484,"text":"National Park Service, Great Smoky Mountains National Park","active":true,"usgs":false}],"preferred":false,"id":900033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lubinski, Barbara A. 0000-0003-3568-2569","orcid":"https://orcid.org/0000-0003-3568-2569","contributorId":202483,"corporation":false,"usgs":true,"family":"Lubinski","given":"Barbara","email":"","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":900034,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fitzpatrick, Benjamin M.","contributorId":336140,"corporation":false,"usgs":false,"family":"Fitzpatrick","given":"Benjamin","email":"","middleInitial":"M.","affiliations":[{"id":80760,"text":"1. Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee","active":true,"usgs":false}],"preferred":false,"id":900035,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70264784,"text":"70264784 - 2024 - Environmental DNA dynamics of three species of unionid freshwater mussels","interactions":[],"lastModifiedDate":"2025-03-24T15:21:21.274494","indexId":"70264784","displayToPublicDate":"2024-04-24T08:17:43","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5840,"text":"Environmental DNA","active":true,"publicationSubtype":{"id":10}},"title":"Environmental DNA dynamics of three species of unionid freshwater mussels","docAbstract":"North American freshwater mussels are of special conservation concern due to their high endemism and the multiple anthropogenic stressors affecting them. Of the over 300 species in North America, nearly one third of these species are federally listed as threatened or endangered. Environmental DNA (eDNA) analysis has been successful in detecting freshwater mussels and could aid in monitoring their populations. Production and degradation rates of eDNA for the species of interest are needed to inform interpretation of eDNA detections, allow possible modeling of relative abundance and population location, and aid in mussel conservation through population identification. Here, we designed and tested qPCR assays for three freshwater mussel species, mucket (Ortmanniana ligamentina), fatmucket (Lampsilis siliquoidea), and the federally endangered spectaclecase (Cumberlandia monodonta). We performed laboratory experiments under controlled conditions to measure eDNA shedding and degradation rates for each species. Different biomasses, temperatures, and food regimens were tested independently to determine if these factors influence the amount of DNA produced by the mussels. Degradation rates of eDNA were measured from experimental tank water after mussels were removed. Overall, we observed low eDNA shedding rates for freshwater mussels compared to previous studies of fish eDNA shedding rates. Furthermore, temperature and feeding showed limited or no significant effects in the species studied. Environmental DNA degradation rates were consistent with those reported in the literature for other taxa. Collectively, our results will be useful for designing eDNA monitoring studies, modeling eDNA dispersal, and interpreting eDNA results to help inform freshwater mussel conservation efforts.
","language":"English","publisher":"Wiley","doi":"10.1002/edn3.543","usgsCitation":"Ruiz-Ramos, D., Thompson, N., Richter, C.A., Voshage, M., Schreier, T.M., Merkes, C.M., and Klymus, K.E., 2024, Environmental DNA dynamics of three species of unionid freshwater mussels: Environmental DNA, v. 6, no. 2, e543, 15 p., https://doi.org/10.1002/edn3.543.","productDescription":"e543, 15 p.","ipdsId":"IP-157659","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":488376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/edn3.543","text":"Publisher Index Page"},{"id":483719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruiz-Ramos, Dannise","contributorId":332474,"corporation":false,"usgs":false,"family":"Ruiz-Ramos","given":"Dannise","affiliations":[{"id":78382,"text":"formerly Columbia Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":931669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":931670,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richter, Catherine A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":138994,"corporation":false,"usgs":true,"family":"Richter","given":"Catherine","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":931671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Voshage, Megan C.","contributorId":332475,"corporation":false,"usgs":false,"family":"Voshage","given":"Megan C.","affiliations":[{"id":78382,"text":"formerly Columbia Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":931672,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schreier, Theresa M. 0000-0001-7722-6292 tschreier@usgs.gov","orcid":"https://orcid.org/0000-0001-7722-6292","contributorId":3344,"corporation":false,"usgs":true,"family":"Schreier","given":"Theresa","email":"tschreier@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":931673,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":931674,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Klymus, Katy E. 0000-0002-8843-6241 kklymus@usgs.gov","orcid":"https://orcid.org/0000-0002-8843-6241","contributorId":5043,"corporation":false,"usgs":true,"family":"Klymus","given":"Katy","email":"kklymus@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":931675,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70257757,"text":"70257757 - 2024 - Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA","interactions":[],"lastModifiedDate":"2024-09-09T16:49:25.320965","indexId":"70257757","displayToPublicDate":"2024-04-24T07:02:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2824,"text":"Natural Hazards and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Characteristics of debris-flow-prone watersheds and debris-flow-triggering rainstorms following the Tadpole Fire, New Mexico, USA","docAbstract":"Moderate- or high-severity fires promote increases in runoff and erosion, leading to a greater likelihood of extreme geomorphic responses, including debris flows. In the first several years following fire, the majority of debris flows initiate when runoff rapidly entrains sediment on steep slopes. From a hazard perspective, it is important to be able to anticipate when and where watershed responses will be dominated by debris flows rather than flood flows. Rainfall intensity averaged over a 15 min duration, I15, in particular, has been identified as a key predictor of debris flow likelihood. Developing effective warning systems and predictive models for post-fire debris flow hazards therefore relies on high-temporal resolution rainfall data at the time debris flows initiate. In this study, we documented the geomorphic response of a series of watersheds following a wildfire in western New Mexico, USA, with an emphasis on constraining debris flow timing within rainstorms to better characterize debris-flow-triggering rainfall intensities. We estimated temporal changes in soil hydraulic properties and ground cover in areas burned at different severities over >2 years to offer explanations for observed differences in spatial and temporal patterns in debris flow activity. We observed 16 debris flows, all of which initiated during the first several months following the fire. The average recurrence interval of the debris-flow-triggering I15 is 1.3 years, which highlights the susceptibility of recently burned watersheds to runoff-generated debris flows in this region. All but one of the debris flows initiated in watersheds burned primarily at moderate or high soil burn severity. Since soil hydraulic properties appeared to be relatively resilient to burning, we attribute reduced debris flow activity at later times to decreases in the fraction of bare ground. Results provide additional constraints on the rainfall characteristics that promote post-fire debris flow initiation in a region where fire size and severity have been increasing.
Carotenoid-dependent ornaments can reflect animals’ diet and foraging behaviors. However, this association should be spatially flexible and variable among populations to account for geographic variation in optimal foraging behaviors. We tested this hypothesis using populations of a marine predator (the brown booby, Sula leucogaster) that forage across a gradient in ocean depth in and near the Gulf of California. Specifically, we quantified green chroma for two skin traits (foot and gular color) and their relationship to foraging location and diet of males, as measured via global positioning system tracking and stable carbon isotope analysis of blood plasma. Our three focal colonies varied in which foraging attributes were linked to carotenoid-rich ornaments. For gular skin, our data showed a shift from a benthic prey-green skin association in the shallow waters in the north to a pelagic prey-green skin association in the deepest waters to the south. Mean foraging trip duration and distance of foraging site from coast also predicted skin coloration in some colonies. Finally, brown booby colonies varied in which trait (foot versus gular skin color) was associated with foraging metrics. Overall, our results indicate that male ornaments reflect quality of diet and foraging–information that may help females select mates who are adapted to local foraging conditions and therefore, are likely to provide better parental care. More broadly, our results stress that diet-dependent ornaments are closely linked to animals’ environments and that we cannot assume ornaments or ornament signal content are ubiquitous within species, even when ornaments appear similar among populations.
Eggshell thickness can be an indicator of environmental pollution in wild birds and shell quality in wild and domestic birds, but it is difficult to measure calcite eggshell thickness due to the presence of the adherent outer eggshell membrane. Eggshells of 13 waterbird species were divided in half longitudinally and the outer membrane was removed from one of the halves. Subsequently, we measured eggshell thickness, both with and without the outer eggshell membrane, using a Hall-effect thickness gauge to the nearest 0.001 mm along the equator of each eggshell half. Outer eggshell membrane thicknesses ranged from 0.014 to 0.073 mm. Caspian Tern (Hydroprogne caspia) and California Gull (Larus californicus) had the thickest eggshell membranes (0.056 and 0.073 mm, respectively), and Green Heron (Butorides virescens) and Killdeer (Charadrius vociferus) had the thinnest membranes (0.014 and 0.022 mm, respectively). The eggshell membrane, as a percent of the total eggshell and membrane thickness, varied among the 13 species and ranged among species from 7.9% to 20.6%. The outer membrane comprised a greater percent of the total eggshell and membrane thickness for Black Skimmer (19.3%; Rynchops niger), California Gull (20.5%), and Forster's Tern (20.6%; Sterna forsteri) than for Green Heron (7.9%), Double-crested Cormorant (10.4%; Phalacrocorax auritus), and Western Grebe (10.6%; Aechmophorus occidentalis). Within species, the outer membrane thickness was not correlated with egg morphometrics but, for a subset of species, there was some indication that the calcite eggshell thickness decreases with embryo development (age). We discuss several reasons for conducting future eggshell thickness measurements without removing the membrane.
","language":"English","publisher":"Wilson Ornithological Society","doi":"10.1676/23-00017","usgsCitation":"Santolo, G.M., Peterson, S.H., Cooney, B., Hartman, C.A., Herzog, M.P., and Ackerman, J.T., 2024, Eggshell membrane thickness and its contribution to total eggshell thickness for 13 waterbird species: The Wilson Journal of Ornithology, v. 136, no. 1, p. 62-76, https://doi.org/10.1676/23-00017.","productDescription":"15 p.","startPage":"62","endPage":"76","ipdsId":"IP-159812","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":434977,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HJJ07G","text":"USGS data release","linkHelpText":"Egg Membrane Thickness in 13 Waterbird Species"},{"id":428729,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"136","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Santolo, Gary M.","contributorId":336702,"corporation":false,"usgs":false,"family":"Santolo","given":"Gary","email":"","middleInitial":"M.","affiliations":[{"id":80834,"text":"Jacobs","active":true,"usgs":false}],"preferred":false,"id":900818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peterson, Sarah H. 0000-0003-2773-3901 sepeterson@usgs.gov","orcid":"https://orcid.org/0000-0003-2773-3901","contributorId":167181,"corporation":false,"usgs":true,"family":"Peterson","given":"Sarah","email":"sepeterson@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":900819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cooney, Breanne","contributorId":336703,"corporation":false,"usgs":false,"family":"Cooney","given":"Breanne","affiliations":[{"id":37814,"text":"Former USGS","active":true,"usgs":false}],"preferred":false,"id":900820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartman, C. Alex 0000-0002-7222-1633 chartman@usgs.gov","orcid":"https://orcid.org/0000-0002-7222-1633","contributorId":131157,"corporation":false,"usgs":true,"family":"Hartman","given":"C.","email":"chartman@usgs.gov","middleInitial":"Alex","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":900821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herzog, Mark P. 0000-0002-5203-2835 mherzog@usgs.gov","orcid":"https://orcid.org/0000-0002-5203-2835","contributorId":131158,"corporation":false,"usgs":true,"family":"Herzog","given":"Mark","email":"mherzog@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":900822,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ackerman, Joshua T. 0000-0002-3074-8322","orcid":"https://orcid.org/0000-0002-3074-8322","contributorId":202848,"corporation":false,"usgs":true,"family":"Ackerman","given":"Joshua","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":900823,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70254125,"text":"70254125 - 2024 - Wetland creation and reforestation of legacy surface mines in the Central Appalachian Region (USA): A potential climate-adaptation approach for pond-breeding amphibians?","interactions":[],"lastModifiedDate":"2024-05-08T11:48:29.128139","indexId":"70254125","displayToPublicDate":"2024-04-24T06:45:15","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Wetland creation and reforestation of legacy surface mines in the Central Appalachian Region (USA): A potential climate-adaptation approach for pond-breeding amphibians?","docAbstract":"This study uses a one-dimensional steady-state hydraulic model and the Fluvial Egg Drift Simulator (FluEgg) to model the drift and dispersion of grass carp eggs and larvae in the Maumee River, Ohio, for 180 scenarios representing different combinations of 10 river flows, 6 water temperatures, and 3 spawning locations. The FluEgg simulations were used to quantify in-river suspended hatching rates (the percentage of eggs that hatch within the river and in suspension) and in-river larval retention rates (the percentage of larvae that reach the gas bladder inflation stage within the river after hatching in suspension), and identify which scenarios produce the highest likelihood of recruitment. The simulations indicate that at low flows, in-river suspended hatching and larval retention rates in the Maumee River are limited by the capacity of the flow to keep fertilized eggs in suspension, whereas at high flows, the limiting factor is the distance available for the eggs/larvae to drift in the river. A wide range of scenarios result in eggs hatching within the river, but all larvae drift into Maumee Bay prior to the gas bladder inflation stage when flows exceed the mean annual flow. The simulations were assessed in the context of the hydraulic conditions that trigger spawning and maximize egg fertilization and the nursery habitat requirements for larval grass carp. The results indicate that the Maumee River, although suitable for grass carp spawning, may not be an ideal setting for recruitment unless Maumee Bay provides adequate nursery habitat for larvae.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102345","usgsCitation":"LeRoy, J.Z., Doyle, H.F., Jackson, P.R., and Cigrand, C.V., 2024, Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 2—Optimal river conditions for egg and larval drift: Journal of Great Lakes Research, v. 50, 102345, 18 p., https://doi.org/10.1016/j.jglr.2024.102345.","productDescription":"102345, 18 p.","ipdsId":"IP-137490","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439771,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1016/j.jglr.2024.102345","text":"Publisher Index Page"},{"id":428354,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Maumee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.38518414361491,\n 41.68593497191691\n ],\n [\n -83.45979759899954,\n 41.74305978003929\n ],\n [\n -83.66454217332985,\n 41.60155527334871\n ],\n [\n -83.8481872826162,\n 41.471249401310274\n ],\n [\n -84.12554495228478,\n 41.444009750259625\n ],\n [\n -84.20397403397705,\n 41.37227829616401\n ],\n [\n -84.41243215532246,\n 41.2918512649257\n ],\n [\n -84.35316138453301,\n 41.241526198376704\n ],\n [\n -84.09303055462573,\n 41.30190322738565\n ],\n [\n -84.060513011197,\n 41.379455129599705\n ],\n [\n -83.7812377285593,\n 41.39524150503729\n ],\n [\n -83.38518414361491,\n 41.68593497191691\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"LeRoy, Jessica Z. 0000-0003-4035-6872 jzinger@usgs.gov","orcid":"https://orcid.org/0000-0003-4035-6872","contributorId":174534,"corporation":false,"usgs":true,"family":"LeRoy","given":"Jessica","email":"jzinger@usgs.gov","middleInitial":"Z.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doyle, Henry F. 0000-0001-9942-8602 hfdoyle@usgs.gov","orcid":"https://orcid.org/0000-0001-9942-8602","contributorId":243432,"corporation":false,"usgs":true,"family":"Doyle","given":"Henry","email":"hfdoyle@usgs.gov","middleInitial":"F.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900029,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cigrand, Charles V. 0000-0002-4177-7583","orcid":"https://orcid.org/0000-0002-4177-7583","contributorId":201575,"corporation":false,"usgs":true,"family":"Cigrand","given":"Charles","email":"","middleInitial":"V.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900030,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70253134,"text":"dr1193 - 2024 - Calculation of a suspended-sediment concentration-turbidity regression model and flood-ebb suspended-sediment concentration differentials from marshes near Stone Harbor and Thompsons Beach, New Jersey, 2018–19 and 2022–23","interactions":[],"lastModifiedDate":"2026-01-27T17:29:12.572398","indexId":"dr1193","displayToPublicDate":"2024-04-22T12:15:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1193","displayTitle":"Calculation of a Suspended-Sediment Concentration-Turbidity Regression Model and Flood-Ebb Suspended-Sediment Concentration Differentials From Marshes Near Stone Harbor and Thompsons Beach, New Jersey, 2018–19 and 2022–23","title":"Calculation of a suspended-sediment concentration-turbidity regression model and flood-ebb suspended-sediment concentration differentials from marshes near Stone Harbor and Thompsons Beach, New Jersey, 2018–19 and 2022–23","docAbstract":"The U.S. Geological Survey collected water velocity and water quality data from salt marshes in Great Channel, southwest of Stone Harbor, New Jersey, and near Thompsons Beach, New Jersey, to evaluate restoration effectiveness after Hurricane Sandy and monitor postrestoration marsh health. Time series data of turbidity and water velocity were collected from 2018 to 2019 and 2022 to 2023 at both sites. Water samples were collected and analyzed for suspended-sediment concentration (SSC), which was used to derive a regression model to estimate a time series of SSC data from turbidity data. The SSC time series data were then combined with the water velocity data to calculate the flood-ebb SSC differential. This report presents the data collection methods, the repeated median regression model used to estimate SSC from turbidity, and the flood-ebb SSC differential calculations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1193","programNote":"Coastal/Marine Hazards and Resources Program","usgsCitation":"De Meo, O.A., Bales, R.D., Ganju, N.K., Marsjanik, E.D., and Suttles, S.E., 2024, Calculation of a suspended-sediment concentration-turbidity regression model and flood-ebb suspended-sediment concentration differentials from marshes near Stone Harbor and Thompsons Beach, New Jersey, 2018–19 and 2022–23: U.S. Geological Survey Data Report 1193, 12 p., https://doi.org/10.3133/dr1193.","productDescription":"Report: iv, 12 p.; 2 Data Releases","numberOfPages":"12","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-162873","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":499107,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116368.htm","linkFileType":{"id":5,"text":"html"}},{"id":427955,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1193/dr1193.XML"},{"id":427958,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CS5U6N","text":"USGS data release","linkHelpText":"Supplementary data in support of oceanographic and water quality times-series measurements made at Thompsons Beach and Stone Harbor, NJ from September 2018 to February 2023"},{"id":427957,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z0Z8DM","text":"USGS data release","linkHelpText":"Time-series measurements of oceanographic and water quality data collected at Thompsons Beach and Stone Harbor, New Jersey, USA, September 2018 to September 2019 and March 2022 to May 2023"},{"id":427956,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1193/images/"},{"id":427954,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1193/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1193"},{"id":427953,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1193/dr1193.pdf","text":"Report","size":"3.17 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1193"},{"id":427952,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1193/coverthb.jpg"}],"country":"United States","state":"New Jersey","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.65514977013649,\n 39.123992018486376\n ],\n [\n -74.84847491860745,\n 39.123992018486376\n ],\n [\n -74.84847491860745,\n 39.01856524124548\n ],\n [\n -74.65514977013649,\n 39.01856524124548\n ],\n [\n -74.65514977013649,\n 39.123992018486376\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543–1598
Grass carp (Ctenopharyngodon idella) are non-indigenous to North America having been translocated to the United States in the 1960s as a potential non-chemical solution for nuisance aquatic vegetation. Reproductively viable grass carp now exist in many watersheds in the United States. In the Great Lakes basin, grass carp were first discovered in the 1980s with direct confirmation of successful reproduction in 2015 via collection of fertilized grass carp eggs in the Sandusky River. Early life stage monitoring also confirmed reproduction in the Maumee River in 2017. During 2018–2021, no new spawning tributaries were discovered (18 total sampling events in five Great Lakes tributaries). In 2022, fourteen eggs with characteristics similar to grass carp were identified from the Huron River which is a tributary to Lake Erie’s Central Basin. Eggs were identified to species via DNA sequencing and were determined to be grass carp eggs. The confirmation of spawning in the Huron River represents a third spawning tributary in the Lake Erie basin and expands eastward the geographic extent of known grass carp spawning locations. Presently, the ability of the Huron River to support hatching and survival of larval grass carp is unknown. Discovery of the Huron River as a grass carp spawning tributary identifies the value of continued surveillance in Great Lakes tributaries for early life stages and conducting scientific inquiries evaluating the consistency of tributary use and survival of early life stages.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102350","usgsCitation":"Hilling, C.D., Landry, A.J., Roberts, J., Thompson, N., Richter, C.A., Brown, R.E., Mayer, C.M., and Qian, S.S., 2024, First documentation of grass carp spawning in Lake Erie’s Central Basin: Journal of Great Lakes Research, v. 50, no. 3, 102350, 6 p., https://doi.org/10.1016/j.jglr.2024.102350.","productDescription":"102350, 6 p.","ipdsId":"IP-161657","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":434978,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P14HMDBT","text":"USGS data release","linkHelpText":"Grass Carp (Ctenopharyngodon idella) Egg Diameter Estimates from the Huron River (Ohio), 2022"},{"id":428068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Huron River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.4658080331037,\n 41.43488681145024\n ],\n [\n -82.75342636612123,\n 41.43488681145024\n ],\n [\n -82.75342636612123,\n 41.027155764237335\n ],\n [\n -82.4658080331037,\n 41.027155764237335\n ],\n [\n -82.4658080331037,\n 41.43488681145024\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hilling, Corbin David 0000-0003-4040-9516","orcid":"https://orcid.org/0000-0003-4040-9516","contributorId":298946,"corporation":false,"usgs":true,"family":"Hilling","given":"Corbin","email":"","middleInitial":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":899394,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Landry, Adam J.","contributorId":335743,"corporation":false,"usgs":false,"family":"Landry","given":"Adam","email":"","middleInitial":"J.","affiliations":[{"id":51831,"text":"Contractor to USGS Great Lakes Science Center","active":true,"usgs":false}],"preferred":false,"id":899395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Roberts, James 0000-0002-4193-610X jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":899396,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, Nathan 0000-0002-1372-6340 nthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-1372-6340","contributorId":196133,"corporation":false,"usgs":true,"family":"Thompson","given":"Nathan","email":"nthompson@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":899397,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richter, Cathy A. 0000-0001-7322-4206 crichter@usgs.gov","orcid":"https://orcid.org/0000-0001-7322-4206","contributorId":1878,"corporation":false,"usgs":true,"family":"Richter","given":"Cathy","email":"crichter@usgs.gov","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":899398,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Ryan E.","contributorId":332137,"corporation":false,"usgs":false,"family":"Brown","given":"Ryan","email":"","middleInitial":"E.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":899399,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mayer, Christine M.","contributorId":203271,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":899400,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Qian, Song S. 0000-0002-2346-4903","orcid":"https://orcid.org/0000-0002-2346-4903","contributorId":306033,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[{"id":62440,"text":"Department of Environmental Sciences, University of Toledo, Toledo, OH 43606","active":true,"usgs":false}],"preferred":false,"id":899401,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70253177,"text":"70253177 - 2024 - Groundwater sustainability and land subsidence in California’s Central Valley","interactions":[],"lastModifiedDate":"2025-05-09T19:57:50.689646","indexId":"70253177","displayToPublicDate":"2024-04-22T06:38:51","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater sustainability and land subsidence in California’s Central Valley","docAbstract":"Longitudinal dispersal of migratory fish species can be interrupted by factors that fragment rivers, such as dams and reservoirs with incompatible habitats, and indirect alterations to variables, such as water temperature or turbidity. The endangered pallid sturgeon (Scaphirhynchus albus) population in the Upper Missouri River Basin in North Dakota and Montana is an example of such fragmentation and alteration due to the construction of dams. We applied a high-resolution, 2+-dimensional modelling framework composed of hydrodynamic and Lagrangian particle tracking components to simulate pallid sturgeon larval drift and dispersal along a 33-km section of the Upper Missouri River to evaluate three main issues: a comparison between multidimensional models and traditional 1-dimensional models, the sensitivity of hydrodynamics to channel morphology, and the implications of channel morphology on retention and transport-time metrics for larval fish. The results indicate that multidimensional models better represent breakthrough curves of transporting larvae compared to 1-dimensional models, especially for the long tail of slow drifters in the population. Results also indicate that channel morphology and hydraulic complexity play significant roles in larval dispersal with certain flow conditions and channel features increasing larval retention and providing potential management options to increase survival rates by adjusting flow conditions during spawning events. For example, modelling indicates increased retention times at discharges 23–38% daily flow exceedance, coincident with emergence of mid-channel sandbars. Findings additionally emphasize the need for improved understanding of biological factors that affect larval drift and dispersal.
We conceptualize and test a non-intrusive barrier, comprised of an oblique bubble screen (OBS) oriented at an angle to the mean flow, to prevent the downstream dispersal of invasive carp egg surrogates. Three surrogates of different densities and diameters were tested. Secondary flows created by the OBS were tuned to redirect surrogate eggs to facilitate their capture. Surface particle image velocimetry and acoustic Doppler velocimetry were used to characterize secondary flows. We assessed the influence of airflow rate, OBS angle, mean flow velocity, and surrogate density on particle redirection. In general, redirection efficiency improves by increasing the OBS angle with respect to the cross-section. At a mean flow velocity of 0.75 metres per second (m/s), the OBS system redirected up to 60% (%) of positively buoyant particles (specific gravity SG = 0.9, and diameter d = 7.09 millimetres [mm]) and 40% of semi-buoyant particles (SG = 1.001, d = 3.1 mm). Negatively buoyant particles (SG = 1.04, and d = 5.90 mm) were redirected by the physical structure of the diffuser rather than by OBS-induced flow. The study shows that an OBS system can be used to effectively redirect carp-egg surrogates over a wide range of particle sizes and densities, allowing for selective targeting of undesired particles in streams.
Sturgeons are among the most endangered fishes in the world. Identifying habitat use characteristics to inform restoration projects is crucial for recovery. However, small sample sizes, inadequate replication of studies, and limited spatial extents complicate our ability to effectively apply the findings of single studies to endangered species conservation across the larger riverscape. We synthesized information from amphidromous and anadromous sturgeons in North America to identify species-specific knowledge gaps and conduct a quantitative comparison of species–habitat relationships. We provided a qualitative summary of substrate use and synthesized estimates of depth and velocity during spawning and non-spawning activity. Generalized patterns among species were identified, such as spawning in fast water on hard substrate and then using slow water with soft substrate areas when not spawning. We noted species-specific variability during spawning that may be attributed to historical maximum length, egg characteristics, and watershed features. This study provides some of the first estimates of habitat use that can be adapted for many populations. Results can contribute to empirically grounded decision-support tools used to prioritize information needs for recovery.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2023-0222","usgsCitation":"Gilligan-Lunda, E.K., Duarte, A., and Peterson, J., 2024, Habitat use of anadromous and amphidromous sturgeons in North America: A systematic review: Canadian Journal of Fisheries and Aquatic Sciences, v. 81, no. 5, p. 508-524, https://doi.org/10.1139/cjfas-2023-0222.","productDescription":"17 p.","startPage":"508","endPage":"524","ipdsId":"IP-156404","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430211,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"81","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gilligan-Lunda, Erin K.","contributorId":339252,"corporation":false,"usgs":false,"family":"Gilligan-Lunda","given":"Erin","email":"","middleInitial":"K.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":339254,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":903887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903888,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70253174,"text":"70253174 - 2024 - Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States","interactions":[],"lastModifiedDate":"2024-04-23T11:58:30.217736","indexId":"70253174","displayToPublicDate":"2024-04-19T06:55:18","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17467,"text":"Machine Learning with Applications","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States","docAbstract":"The application of Long Short-Term Memory (LSTM) models for streamflow predictions has been an area of rapid development, supported by advancements in computing technology, increasing availability of spatiotemporal data, and availability of historical data that allows for training data-driven LSTM models. Several studies have focused on improving the performance of LSTM models; however, few studies have assessed the applicability of these LSTM models across different hydroclimate regions. This study investigated the single-basin trained local (one model for each basin), multi-basin trained regional (one model for one region), and grand (one model for several regions) models for predicting daily streamflow in water-limited Great Basin (18 basins) and energy-limited New England (27 basins) regions in the United States using the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) data set. The results show a general pattern of higher accuracy in daily streamflow predictions from the regional model when compared to local or grand models for most basins in the New England region. For the Great Basin region, local models provided smaller errors for most basins and substantially lower for those basins with relatively larger errors from the regional and grand models. The evaluation of one-layer and three-layer LSTM network architectures trained with 1-day lag information indicates that the addition of model complexity by increasing the number of layers may not necessarily increase the model skill for improving streamflow predictions. Findings from our study highlight the strengths and limitations of LSTM models across contrasting hydroclimate regions in the United States, which could be useful for local and regional scale decisions using standalone or potential integration of data-driven LSTM models with physics-based hydrological models.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Climate change and climate variability impacts such as rising sea levels have the potential to exacerbate seawater intrusion and the strain on coastal freshwater resources in already stressed groundwater basins such as those in the Pajaro Valley groundwater basin, California. Regional hydrologic models are often coupled with climate projections to forecast future hydrologic conditions and inform adaptive resources management strategies. However, there is high uncertainty in the future projections of water resources due to uncertainties from downscaling global general circulation models (GCMs) to local scale climate change projections, future land use changes, and the inherent uncertainty of developed hydrologic models. Future climate projections and the magnitude of their influence on modeled hydrologic drivers are highly variable. Therefore, to develop a forecast model, an ensemble of different projections can be used to capture a wider range of basin responses and the associated uncertainties in the modeled forecasts. Understanding the reliability and uncertainty of forecasts is important for developing climate adaptation strategies such as developing protective thresholds, particularly at the basin scale where the impacts are felt, and adaptation is implemented. To demonstrate this, an uncertainty analysis of groundwater level and seawater intrusion forecasts for the Pajaro Valley groundwater basin was performed using an ensemble of three future climate projections with the Pajaro Valley Integrated Hydrologic Model (PVIHM) and the first-order second moment (FOSM) method. FOSM uncertainty analysis of hydrologic forecasts across a multi-GCM climate ensemble provides an upper and lower bound of potential impacts of climate change on sustainability targets related to mitigating seawater intrusion. The groundwater level forecasts’ narrow range of variability can help policymakers in adaptation planning by constraining possible outcomes to a focused range for risk-management decisions. However, less than one-third of groundwater level forecasts met the current protection thresholds to prevent chronic lowering of groundwater. Therefore, sustainability targets may need to be reassessed. Relative to groundwater level changes, the seawater intrusion forecasts had larger uncertainty due to the GCM climate projections and the simulated hydrologic response that were compounded by the propagation of scaling and bias from the GCMs and model simplifications in simulating the coastal boundary.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2024.131226","usgsCitation":"Earll, M.M., Henson, W.R., Lockwood, B., and Boyce, S.E., 2024, Evaluating seawater intrusion forecast uncertainty under climate change in the Pajaro Valley, California: Journal of Hydrology, v. 636, 131226, 17 p., https://doi.org/10.1016/j.jhydrol.2024.131226.","productDescription":"131226, 17 p.","ipdsId":"IP-118873","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":484763,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Pajaro Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.92596725165258,\n 37.07637156314877\n ],\n [\n -121.92596725165258,\n 36.716849299804664\n ],\n [\n -121.42957626930755,\n 36.716849299804664\n ],\n [\n -121.42957626930755,\n 37.07637156314877\n ],\n [\n -121.92596725165258,\n 37.07637156314877\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"636","noUsgsAuthors":false,"publicationDate":"2024-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Earll, Marisa M. 0000-0002-4367-2013 mearll@usgs.gov","orcid":"https://orcid.org/0000-0002-4367-2013","contributorId":223723,"corporation":false,"usgs":true,"family":"Earll","given":"Marisa","email":"mearll@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henson, Wesley R. 0000-0003-4962-5565 whenson@usgs.gov","orcid":"https://orcid.org/0000-0003-4962-5565","contributorId":384,"corporation":false,"usgs":true,"family":"Henson","given":"Wesley","email":"whenson@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lockwood, Brian","contributorId":80202,"corporation":false,"usgs":true,"family":"Lockwood","given":"Brian","email":"","affiliations":[],"preferred":false,"id":933960,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyce, Scott E. 0000-0003-0626-9492 seboyce@usgs.gov","orcid":"https://orcid.org/0000-0003-0626-9492","contributorId":4766,"corporation":false,"usgs":true,"family":"Boyce","given":"Scott","email":"seboyce@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":933820,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70257269,"text":"70257269 - 2024 - Updates to the Flow Photo Explorer tool","interactions":[],"lastModifiedDate":"2024-08-28T14:19:25.771938","indexId":"70257269","displayToPublicDate":"2024-04-18T09:15:11","publicationYear":"2024","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":18358,"text":"Flow Photo Explorer","active":true,"publicationSubtype":{"id":30}},"title":"Updates to the Flow Photo Explorer tool","docAbstract":"No abstract available.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Fair, J.H., Letcher, B., and Goodling, P.J., 2024, Updates to the Flow Photo Explorer tool: Flow Photo Explorer, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-164874","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":433247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":433246,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://content.govdelivery.com/accounts/USDOIGS/bulletins/397081f","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fair, Jennifer H. 0000-0002-9902-1893","orcid":"https://orcid.org/0000-0002-9902-1893","contributorId":245941,"corporation":false,"usgs":true,"family":"Fair","given":"Jennifer","middleInitial":"H.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":909818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Letcher, Benjamin 0000-0003-0191-5678","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":242666,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":909819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goodling, Phillip J. 0000-0001-5715-8579","orcid":"https://orcid.org/0000-0001-5715-8579","contributorId":239738,"corporation":false,"usgs":true,"family":"Goodling","given":"Phillip","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":909820,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70253895,"text":"70253895 - 2024 - Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling","interactions":[],"lastModifiedDate":"2024-05-20T15:42:40.394048","indexId":"70253895","displayToPublicDate":"2024-04-18T09:07:55","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling","title":"Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling","docAbstract":"Control of invasive grass carp (Ctenopharyngodon idella) populations in the Western Lake Erie Basin merits adaptive management guided by the best available science. Presently (2024), capture of mature grass carp in rivers during spawning season is most efficient, so knowing when and where grass carp are spawning is essential information for natural resource agencies. Using bidirectional drift modeling and grass carp ichthyoplankton samples captured in the Maumee River during the 2017–2019 spawning seasons, this study identified 12 probable grass carp spawning areas in the lower 96.5-kilometers of the Maumee River. These spawning areas were located both above and below the Grand Rapids/Providence low-head dams. Three areas showed evidence of multiyear use, while nine had multi-event use. Spawning activity had no definitive diel variation and occurred at an average photoperiod of 15.15 h. The maturation metric ADD15, or annual degree days above 15 degrees Celsius, generally exceeded the 655 threshold for spawning; however, some spawning occurred when ADD15 ≤235, indicating spawners likely matured in a warmwater discharge. The probable spawning areas were generally characterized by mean velocities between 0.4 and 2.1 m per second (with locally higher velocities possible), areas of high turbulence produced by dam spillways or bedrock outcroppings, channel constrictions, confluences, islands, and bridges with piers in the water. Spawning suitability indices (SSI), based on velocity, varied considerably between spawning areas and SSI models. These results could be used to inform control efforts and predict potential grass carp spawning locations in other rivers under threat of invasion.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102347","usgsCitation":"Jackson, P.R., Cigrand, C.V., Kocovsky, P.M., King, N.R., Kasprak, A., Lindroth, E., Doyle, H.F., Qian, S.S., and Mayer, C.M., 2024, Reproduction of grass carp (Ctenopharyngodon idella) in the Maumee River, Ohio: Part 1—Spawning area identification using bidirectional drift modeling: Journal of Great Lakes Research, v. 50, 102347, 17 p., https://doi.org/10.1016/j.jglr.2024.102347.","productDescription":"102347, 17 p.","ipdsId":"IP-142521","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":439799,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2024.102347","text":"Publisher Index Page"},{"id":434983,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CQ6FYX","text":"USGS data release","linkHelpText":"Hydraulic Model Archive and Fluvial Egg Drift Simulator (FluEgg) Results for Simulations of Invasive Carp Egg and Larval Drift in the Maumee River, Ohio (ver. 1.1, July 2023)"},{"id":428353,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Ohio","otherGeospatial":"Maumee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.38518414361491,\n 41.68593497191691\n ],\n [\n -83.45979759899954,\n 41.74305978003929\n ],\n [\n -83.66454217332985,\n 41.60155527334871\n ],\n [\n -83.8481872826162,\n 41.471249401310274\n ],\n [\n -84.12554495228478,\n 41.444009750259625\n ],\n [\n -84.20397403397705,\n 41.37227829616401\n ],\n [\n -84.41243215532246,\n 41.2918512649257\n ],\n [\n -84.35316138453301,\n 41.241526198376704\n ],\n [\n -84.09303055462573,\n 41.30190322738565\n ],\n [\n -84.060513011197,\n 41.379455129599705\n ],\n [\n -83.7812377285593,\n 41.39524150503729\n ],\n [\n -83.38518414361491,\n 41.68593497191691\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cigrand, Charles V. 0000-0002-4177-7583","orcid":"https://orcid.org/0000-0002-4177-7583","contributorId":201575,"corporation":false,"usgs":true,"family":"Cigrand","given":"Charles","email":"","middleInitial":"V.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900019,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kocovsky, Patrick M. 0000-0003-4325-4265 pkocovsky@usgs.gov","orcid":"https://orcid.org/0000-0003-4325-4265","contributorId":3429,"corporation":false,"usgs":true,"family":"Kocovsky","given":"Patrick","email":"pkocovsky@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":251,"text":"Ecosystems Mission Area","active":false,"usgs":true}],"preferred":true,"id":900020,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"King, Nicole R.","contributorId":239495,"corporation":false,"usgs":false,"family":"King","given":"Nicole","email":"","middleInitial":"R.","affiliations":[{"id":47892,"text":"University of Toledo Lake Erie Center, 6200 Bay Shore Road, Oregon, OH","active":true,"usgs":false}],"preferred":false,"id":900021,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":245742,"corporation":false,"usgs":false,"family":"Kasprak","given":"Alan","affiliations":[{"id":49307,"text":"Current: Utah State University. Former: Southwest Biological Science Center, Grand Canyon Monitoring and Research Center, U.S. Geological Survey, Flagstaff, AZ 86001, USA","active":true,"usgs":false}],"preferred":false,"id":900022,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lindroth, Evan M. 0000-0002-9746-4359","orcid":"https://orcid.org/0000-0002-9746-4359","contributorId":336138,"corporation":false,"usgs":false,"family":"Lindroth","given":"Evan M.","affiliations":[{"id":80757,"text":"Maricopa County Flood Control District","active":true,"usgs":false}],"preferred":false,"id":900023,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Doyle, Henry F. 0000-0001-9942-8602 hfdoyle@usgs.gov","orcid":"https://orcid.org/0000-0001-9942-8602","contributorId":243432,"corporation":false,"usgs":true,"family":"Doyle","given":"Henry","email":"hfdoyle@usgs.gov","middleInitial":"F.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":900024,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Qian, Song S. 0000-0002-2346-4903","orcid":"https://orcid.org/0000-0002-2346-4903","contributorId":306033,"corporation":false,"usgs":false,"family":"Qian","given":"Song","email":"","middleInitial":"S.","affiliations":[{"id":62440,"text":"Department of Environmental Sciences, University of Toledo, Toledo, OH 43606","active":true,"usgs":false}],"preferred":false,"id":900025,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Mayer, Christine M.","contributorId":203271,"corporation":false,"usgs":false,"family":"Mayer","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":900026,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70253125,"text":"70253125 - 2024 - Network connectivity contributes to native small-bodied fish assemblages in the upper Mississippi River system","interactions":[],"lastModifiedDate":"2024-06-03T15:03:49.975123","indexId":"70253125","displayToPublicDate":"2024-04-18T07:15:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17465,"text":"Journal of Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Network connectivity contributes to native small-bodied fish assemblages in the upper Mississippi River system","docAbstract":"In a conservation context, identifying key habitats suitable for reproduction, foraging, or survival is a useful tool, yet challenging for species with large geographic distributions and/or living in remote regions.
The objective of this study is to identify selected habitats at multiple levels and scales of the threatened eastern North American population of golden eagles (Aquila chrysaetos). We studied habitat selection at three levels: landscape (second order of selection), foraging (third order of selection), and nesting (fourth order of selection).
Using tracking data from 30 adults and 366 nest coordinates spanning over a 1.5 million km2 area in remote boreal and Arctic regions, we modelled the three levels of habitat selection with resource selection functions using seven environmental features (aerial, topographical, and land cover). We then calculated the relative probability of selection in the study area to identify regions with higher probabilities of selection.
Eagles selected more for terrain ruggedness index and relative elevation than land cover (i.e., forest cover, distance to water; mean difference in relative selection strength: 1.2 [0.71; 1.69], 95% CI) at all three levels. We also found that the relative probability of selection at all three levels was ~ 25% higher in the Arctic than in the boreal regions. Eagles breeding in the Arctic travelled shorter foraging distances with greater access to habitat with a high probability of selection than boreal eagles.
Here we found which aerial and topographical features were important for several of the eagles’ life cycle needs. We also identified important areas to monitor and preserve this threatened population. The next step is to quantify the quality of habitat by linking our multi-level, multi-scale approach to population demography and performance such as reproductive success.
The location of estuarine organisms varies based on geophysical cycles and environmental conditions, which can strongly bias understanding of organism abundance and distribution. In the San Francisco Estuary, California, extensive monitoring surveys have provided insight into the life history and ecology of certain commercially important or legislatively protected fish species. However, there remains substantial uncertainty in factors influencing the vertical and lateral distributions of many other nekton species in the San Francisco Estuary, including longfin smelt Spirinchus thaleichthys, for whom such distributional information may highly influence interpretation of existing data. We carried out paired sampling using surface and demersal gears to address three questions: (1) Does diel phase influence the vertical position of nekton (e.g., surface versus demersal)? (2) Do environmental conditions, specifically turbidity, influence the vertical and lateral positions of nekton (e.g., center channel versus peripheral shoal)? (3) Does tidal variability influence vertical and lateral distributions of nekton? We documented variability in sampled nekton densities across diel phase (day/night), vertical position (surface/bottom), and lateral position (channel/shoal). Tidal phase and turbidity concentration influenced vertical and lateral distributions for some species at certain locations. Although infrequently encountered, we documented associations of longfin smelt with the lower water column and shoal habitats, with some evidence for upward vertical shifts in low light conditions brought about by nightfall or elevated turbidity. Observed habitat associations provide insight into how interacting geophysical and environmental factors may influence the distribution of nekton and thus the vulnerability of individual species to detection by sampling gears.
Nutrient (nitrogen [N] and phosphorus [P] chemistry) downgradient from onsite wastewater treatment system (OWTS) was evaluated with a groundwater study in the area surrounding Elizabeth Lake, the largest of three sag lakes within the Santa Clara River watershed of Los Angeles County, California.
Elizabeth Lake is listed on the “303 (d) Impaired Waters List” for excess nutrients and is downgradient from more than 600 OWTS. The primary objective of this study was to develop a conceptual hydrogeological model to determine if discharge from OWTS is transported into shallow groundwater within the Elizabeth Lake subwatershed and contributes nutrients to Elizabeth Lake in excess of the total maximum daily load limit. An analysis of historical data and data collected for this study provided estimates of aquifer properties, such as hydraulic gradients and other parameters necessary to estimate boundary conditions. Electrical resistivity tomography (ERT) surveys were done to determine the best monitoring well locations and to estimate depth to groundwater. During 4 separate sampling events, 11 wells, 2 imported water tanks, 1 spring (sampled on March 17, 2019), and Elizabeth Lake were sampled, which occurred during February–September 2020.
ERT transects and borehole geophysical measurements indicated that there were low to high resistivity materials in the subsurface and potential perched fresh water. Most of the aquifer material was characterized as sandy silt, occasionally with mixed clays and medium gravels, and was estimated to have a hydraulic conductivity from 3.28x10−3 to 16.4 feet per day, a porosity from 0.34 to 0.42, and a hydraulic gradient from 0.01 to 0.03. Although bedrock was not obvious in ERT transects, all well depths were terminated at depths of an impassible confining layer observed to be a highly consolidated blue-gray clay. Depths to granitic bedrock, based on road outcrops and lithologic driller logs, varied throughout the study area. Depth to the bedrock was estimated to be shallow on the north side of Elizabeth Lake at approximately 30 feet below land surface (ft bls). Depth to bedrock is at 50 ft bls toward the east of the Elizabeth Lake subwatershed, which is at topographic ground surface to the north and south of the residential development. Groundwater levels ranged from approximately 0 to 12 ft bls during this study. Historical water levels ranged from 8 to 16 ft bls in the lower elevation of the study area and increased to depths of as much as 80 ft bls at higher elevations on the north and south boundaries of the Elizabeth Lake subwatershed.
Water-quality samples were analyzed for major ions, nutrients, dissolved organic carbon, stable isotopes, and age-dating tracers. A principal component analysis was completed to determine organic matter sources. The proportion of recharge from imported waters, used for domestic consumption, was calculated using stable water isotopes, deuterium (δD) and oxygen (δ18O). Recharge from imported waters accounted for approximately 15–71 percent of the total recharge to groundwater within the study area. Total nitrogen concentrations ranged from 0.17 to 30.9 milligrams per liter (mg/L) as N, and phosphorus, measured in the soluble form as orthophosphate, ranged from 0.03 to 0.35 mg/L as P. Nitrate concentrations in groundwater samples ranged from less than the detection limit (0.01 mg/L as N) to approximately 24 mg/L as N. Nitrate was not detected in 3 of the 12 sites sampled during the study (2 wells and Elizabeth Lake). Dissolved organic carbon concentrations ranged from 0.4 to 27 mg/L in groundwater and from 9.9 to 100 mg/L in Elizabeth Lake. Ammonium and orthophosphate concentrations generally were low in groundwater. However, elevated concentrations of ammonium in Elizabeth Lake were assumed to be due to avian waste products or biological nitrogen fixation. Groundwater ages were mostly modern (recharged since 1952), with a median recharge temperature of 13 degrees Celsius.
Redox conditions in groundwater indicated the likely occurrence of nitrate attenuation by denitrification downgradient from the wells to the south of Elizabeth Lake before groundwater discharges to the lake. Undetectable nitrate in Elizabeth Lake at the time of sampling was likely due to algal uptake. Most wells contained stable isotopes of nitrogen and oxygen in nitrate (δ15N-NO3 and δ18O-NO3) molecules with values consistent with denitrification. However, one monitoring well on the north of Elizabeth Lake (ELLA-8) had no evidence of denitrification, based on elevated concentrations of nitrate and a sufficient amount of dissolved oxygen such that the water was oxic and not favorable for the denitrification reaction. Consequently, this nitrate could be delivered to Elizabeth Lake through groundwater discharge if nitrate is not removed from the system by denitrifying bacteria downgradient from the well before the groundwater discharges into Elizabeth Lake. The principal component analysis demonstrated that dissolved organic matter optical properties track different sources of dissolved organic matter from decayed plants, animals, and animal-derived wastes. Two wells contained strong indicators of OWTS water presence, although geochemical evidence indicated other wells may also be affected by OWTS discharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245012","collaboration":"Water Resources Mission Area—National Water Quality ProgramDirector,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
No abstract available.
","language":"English","publisher":"Climate Adaptation Science Centers","usgsCitation":"Drexler, J.Z., 2024, A prioritization plan for coastal wetland restoration on Molokaʻi, 6 p.","productDescription":"6 p.","ipdsId":"IP-165463","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":502267,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://cascprojects.org/#/project/4f8c650ae4b0546c0c397b48/6049171dd34eb120311abdf6"},{"id":502238,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Moloka'i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.3103027088116,\n 21.26520299843331\n ],\n [\n -157.3103027088116,\n 21.03304807165101\n ],\n [\n -156.69434439396318,\n 21.03304807165101\n ],\n [\n -156.69434439396318,\n 21.26520299843331\n ],\n [\n -157.3103027088116,\n 21.26520299843331\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":901586,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70252963,"text":"sir20245018 - 2024 - Hydrologic study of green infrastructure in poorly drained urbanized soils at RecoveryPark, Detroit, Michigan, 2014–21","interactions":[],"lastModifiedDate":"2026-02-03T17:56:18.918058","indexId":"sir20245018","displayToPublicDate":"2024-04-15T13:10:07","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-5018","displayTitle":"Hydrologic Study of Green Infrastructure in Poorly Drained Urbanized Soils at RecoveryPark, Detroit, Michigan, 2014–21","title":"Hydrologic study of green infrastructure in poorly drained urbanized soils at RecoveryPark, Detroit, Michigan, 2014–21","docAbstract":"Uncontrolled stormwater runoff volume is a legacy stressor on sewer-system capacity that is further compromised by the effects of aging infrastructure. Green stormwater infrastructure (GSI) has been used in a variety of designs and configurations (for example, bioretention) with the goal of increasing evapotranspiration and infiltration in the local water cycle. In practice, GSIs have variable effectiveness in reducing runoff volume.
An urban residential site near Detroit, Michigan, called RecoveryPark was monitored for 8 years before and after GSI construction to evaluate how effectively the GSI reduced volumes of stormwater flowing to Detroit’s Water Resource Recovery Facility through combined sewer systems. In addition to the GSI, the study site included an urban farm where salad crops were grown in hoop houses. The monitoring approach was to characterize the urban water cycle through high-frequency measurements of inflows and outflows. Datasets included meteorological data, soils and sediment characteristics, groundwater levels, flows within the combined sewer system, and soils and water chemistry with specific focus on the disposition of road salt.
Although land cover within the RecoveryPark sewershed was high-density residential in the 1950s, the sewershed included only one residence within the 8.74-acre sewershed during this study. Measurements of annual precipitation at the site exceeded long-term annual averages by more than 10 inches during 3 of the 8 years of study. Potential evapotranspiration was often greater than the measured precipitation that averaged 28–34 inches per year. As compared to underlying clay-rich sediments, soils data indicated relatively permeable sediments near land surface with estimated hydraulic conductivity of 0.75 inches per hour; however, these values decreased with increasing depth. Groundwater-level data revealed increases in groundwater storage as indicated by increases in seasonal groundwater levels and development of a groundwater mound adjacent to the GSI. These increases in groundwater levels were directly adjacent to swales designed to infiltrate stormwater and only became evident after installing the GSI.
Flows within the combined sewer system included rainwater, septic effluent, groundwater infiltration, leakage from water-supply lines, and release of water stored in abandoned foundations. Dry-weather flows (no rain fell within the prior 3 days) averaged 7–10 gallons per minute, which were much greater than flows estimated by septic outflow alone. A set of estimated water budgets were compiled, and results showed large discrepancies in unaccounted flows. To further examine these discrepancies, dye-tracing within the combined sewer system helped examine the sources of water by relating flow volumes to drainage area. For one of the monitoring sites within the combined sewer system along the southeast side of the study area, flows estimated by dye concentrations were more than 10 percent greater than those measured by standard methods. Through peak-flow-regression analysis, a minimum of 2.4 million gallons of water per year were infiltrated or lost to evapotranspiration because of GSI construction. After site modifications were made by excavating gravel drains to improve drainage characteristics, estimated stormwater volumes within the combined sewer system returned to near preconstruction levels. The GSI was effectively bypassed to address slow infiltration rates and standing water; the bypass all but eliminated the potential benefits of volume reduction.
Late in the project, a water-quality study was added to examine the transport of road salt and associated chloride within the GSI and the combined sewer system. Continuous specific conductance was used as a surrogate for chloride concentrations to estimate that 2,790 pounds of dissolved chloride passed through the sewershed during the winter months of late 2020 through early 2021. These data were collected after GSI modification, therefore most, if not all, of the chloride was transported directly to Detroit’s Water Resource Recovery Facility via the combined sewer system. Mixing diagrams using chloride and bromide concentrations of road salt, potable water, rainwater, groundwater, and water from the combined sewer system confirmed that water within the combined sewer system is a mix of these sources. The poor condition of the combined sewer system pipes and resulting unaccounted inflows added to the challenge of accurately monitoring and identifying sources and sinks of water within the RecoveryPark sewershed.
Our research results suggest that—along with clear and quantifiable objectives—the catchment and site conditions should be well-characterized before determining the GSI design. In addition, the work presented in this report provides implications and lessons learned for effectiveness and future studies of GSI in urban settings. These efforts can be improved through increased communication between stakeholders, use of high-quality soils in GSI that have suitable hydraulic characteristics, redundant data-collection networks for critical data streams, and focusing meteorological-data collection within the GSI to obtain relevant evapotranspiration data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245018","collaboration":"Prepared in cooperation with United States Environmental Protection Agency","usgsCitation":"Haefner, R.J., Hoard, C.J., and Shuster, W., 2024, Hydrologic study of green infrastructure in poorly drained urbanized soils at RecoveryPark, Detroit, Michigan, 2014–21: U.S. Geological Survey Scientific Investigations Report 2024–5018, 29 p., https://doi.org/10.3133/sir20245018.","productDescription":"Report: viii, 29 p.; Dataset; 2 Data Releases","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-154558","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":499447,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_116361.htm","linkFileType":{"id":5,"text":"html"}},{"id":427762,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96GBEXW","text":"USGS data release","linkHelpText":"Select pipe-flow monitoring data from RecoveryPark in Detroit, MI (2015–2016)"},{"id":427761,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FP21N9","text":"USGS data release","linkHelpText":"Select pipe-flow monitoring data from RecoveryPark in Detroit, MI (2015–2021)"},{"id":427759,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5018/images/"},{"id":427758,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5018/sir20245018.XML"},{"id":427757,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5018/sir20245018.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5018"},{"id":427756,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5018/coverthb.jpg"},{"id":427763,"rank":8,"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":427760,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245018/full"}],"country":"United States","state":"Michigan","city":"Detroit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.0495294507557,\n 42.37379642239543\n ],\n [\n -83.0495294507557,\n 42.36384762590089\n ],\n [\n -83.0332708011147,\n 42.36384762590089\n ],\n [\n -83.0332708011147,\n 42.37379642239543\n ],\n [\n -83.0495294507557,\n 42.37379642239543\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1992 Folwell Avenue
St. Paul, MN 55108