Winter cover crops such as barley, rye, and wheat help to improve soil structure by increasing porosity, aggregate stability, and organic matter, while reducing the loss of agricultural nutrients and sediments into waterways. The environmental performance of cover crops is affected by choice of species, planting date, planting method, nutrient inputs, temperature, and precipitation. The Maryland Department of Agriculture (MDA) oversees an agricultural cost-share program that provides farmers with funding to cover costs associated with planting winter cover crops, and the U.S. Geological Survey (USGS) and the U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) have partnered with the MDA to develop satellite remote sensing techniques for measuring cover crop performance. The MDA has developed the capacity to digitize field boundaries for all fields enrolled in their cover crop programs (>26,000 fields per year) to support a remote sensing performance analysis at a statewide scal,e and has requested assistance with the associated imagery processing from the National Aeronautics and Space Administration (NASA). Using the Google Earth Engine (GEE) cloud computing platform, scripts were developed to process Landsat 5/7/8 and Harmonized Sentinel-2 imagery to measure winter cover crop performance. We calibrated cover crop performance models using linear regression between satellite vegetation indices and USGS / USDA-ARS field sampling data collected on Maryland farms between 2006 and 2012 (1298 samples). Satellite-derived Normalized Difference Vegetation Index (NDVI) values showed significant correlation with the natural logarithm of cover crop biomass (p ≤0.01, R2 = 0.56) and with observed percent vegetative ground cover (p ≤0.01, R2 = 0.68). The GEE scripts were used to composite seasonal maximum NDVI values for each enrolled cover crop field and calculate performance metrics for the winter and spring seasons of three enrollment years (2014–15, 2015–16, and 2017–18) for four Maryland counties. Results from winter 2017–18 demonstrate that rye and barley fields had higher biomass than wheat fields, and that early planting, along with planting methods that increase seed-soil contact, increased performance. The processing capabilities of GEE will support the MDA in scaling up remote sensing performance analysis statewide, providing information to evaluate the environmental outcomes associated with various agronomic management strategies. The tool can be modified for different seasonal cutoffs, utilize new sensors to capture phenology in winter and spring, and scale to larger regions for use in adaptive management of winter cover crops planted for environmental benefit.
Managed and wild pollinators are critical components of agricultural and natural systems. Despite the well-known value of insect pollinators to U.S. agriculture, Apis mellifera (Linnaeus, 1758; honey bees) and wild bees currently face numerous stressors that have resulted in declining health. These declines have engendered support for pollinator conservation efforts across all levels of government, private businesses, and nongovernmental organizations. In 2014, the U.S. Department of Agriculture (USDA) and the U.S. Geological Survey initiated an interagency agreement to evaluate honey bee forage across multiple States in the northern Great Plains and upper Midwest. The long-term goal of this study was to provide an empirical evaluation of floral resources used by honey bees, and the relative contribution of multiple land covers and USDA conservation programs to bee health and productivity. Our multi-State analysis of land-use change from 2006 to 2016 revealed loss of grassland and increases in corn and soybean area in North and South Dakota, representing a significant loss of bee-friendly land covers in areas that support the highest density of summer bee yards in the entire United States. Our landscape models demonstrate the importance of the Conservation Reserve Program in providing safe locations for beekeepers to keep honey bees during the summer and highlights how land use in the northern Great Plains has a lasting effect on the health of honey bee colonies during almond pollination the subsequent spring. Our multiseason, multi-State genetic analysis of honey bee-collected pollen revealed Melilotus spp., Asteraceae, Trifolium spp., Fabaceae, Sonchus arvensis, Symphyotrichum cordifolium, and Solidago spp. were the top taxa detected; Melilotus spp. represented 42 percent of all detected taxa. Symphyotrichum cordifolium, Solidago spp., and Grindelia spp. were the top native forbs detected in honey bee-collected pollen. We also conducted plant and bee surveys on private lands enrolled in the Conservation Reserve Program and Environmental Quality Incentives Program. In general, we found significant variability in floral resources and pollinator utilization across USDA programs and practices. On average, greater than 75 percent of honey bee flower observations on private lands enrolled in a USDA conservation program were on non-native forbs, whereas 33 percent of wild bee flower observations were on non-native forbs. Melilotus officinalis and Medicago sativa were the most visited by honey bees, wherease Medicago sativa and Helianthus maximiliani were the most visited by wild bees. Our analysis of nectar dearth periods in June and September for honey bees revealed that although Melilotus officinalis and Medicago sativa were highly visited, less common native forb species such as Ratibida columnifera, Agastache foeniculum, and Gaillardia aristata were preferred species. However, these preferred species were relatively rare on the landscape and are, therefore, unlikely to make up a sizable part of the honey bee diet. In addition to our empirical results, we also showcase how the U.S. Geological Survey Pollinator Library, a decision-support tool for natural resource managers, can be used to design cost-effective seeding mixes for pollinators. Collectively, the results of this research will assist USDA with maximizing the ecological impact and cost-effectiveness of their conservation programs on pollinators in the northern Great Plains.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201037","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture","usgsCitation":"Otto, C.R.V., Smart, A., Cornman, R.S., Simanonok, M., and Iwanowicz, D.D., 2020, Forage and habitat for pollinators in the northern Great Plains—Implications for U.S. Department of Agriculture conservation programs: U.S. Geological Survey Open-File Report 2020–1037, 64 p., https://doi.org/10.3133/ofr20201037.","productDescription":"Report: ix, 64 p.; Data Releases","numberOfPages":"78","onlineOnly":"N","ipdsId":"IP-114029","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":376134,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1037/ofr20201037.pdf","text":"Report","size":"6.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1037"},{"id":376135,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Z7DVY4","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Dataset—Molecular identification of honey bee collected pollen in the Northern Great Plains, North America, 2015–2016"},{"id":376136,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O61BCB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Dataset—Plant and bee transects in the Northern Great Plains 2015–2018"},{"id":376133,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1037/coverthb.jpg"}],"country":"United States","state":"Minnesota, North Dakota, South 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\"}}]}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
A field investigation intended to measure the potential for erosion of sediments beside the American and Sacramento Rivers near Sacramento, California, is described. The study featured two primary components: (1) drilling and soil sampling to reveal lithology, down to depths matching the local river thalweg, where possible, and (2) borehole erosion tests (BETs) as described by Briaud and others (2017) at many of the same locations. The latter test involved drilling a vertical hole, measuring its diameter profile, inserting a hollow drilling rod to almost the bottom of the hole, and pumping fluid through the drilling rod at a known discharge for a chosen time interval. The hole was then resurveyed to establish an erosion rate (change in borehole radius divided by duration of flow event) as a function of depth, and the test was repeated. This test was performed with water as the erosive fluid at 12 locations, with 1 test repeated with drilling mud. Lithology holes were drilled at these same locations and an additional five locations. Drilling operations took place on river left and river right on the American River and river left (left bank, when looking downstream) on the Sacramento River.
The drilling to acquire sediment samples and reveal lithology involved the use of a mobile drilling rig equipped with a 6-inch (in.) auger, a 140-pound pneumatic hammer to drive split spoon and Calmod samplers, and a piston to push Shelby tube samplers to obtain samples of clayey material. Blow count (hammer blows per 6-in. sampler advance) was recorded while sampling, and the process was logged using standard U.S. Army Corps of Engineers (USACE), Sacramento District procedures. Sediment samples were identified and described in the field per ASTM D2488 and then delivered to a USACE laboratory and to Texas A&M University for additional laboratory analysis.
The BETs were performed with the same drilling rig that performed the drilling for definition of lithology. In most instances, tests were limited to regions above the water table, to avoid slumping of the borehole and heaving sands pushing into the hole. Most of the tests featured sediments that were primarily silty sand or sandy silt.
The testing procedure involved comparing borehole profiles before and after passing an assumed constant discharge through a drilling rod to the bottom of the drilled hole. Discharge and water losses were logged during the testing procedure, and water losses into the walls of the drilled hole were typically less than 5 percent of the introduced volume. For the tests performed with water, the coefficient of variation of the discharge ranged from 4.5 to 28 percent, with a mean of 13 percent, but the mean discharge appeared to be reasonably steady over the typical test duration of 10–30 minutes. It was thus assumed that discharge was constant and water losses during the tests were neglected. Coefficients of variation of the discharge for the three tests performed with drilling mud were much higher (20–50 percent), but erosion rates were much smaller.
Resolution of the borehole caliper-reported diameter was 0.1 in. and several of the tests lasted for 10 minutes. With boreholes measured twice, before and after each test, and averaged, these numbers correspond to an apparent erosion rate (radius change divided by test duration) of 0.3 inches per hour (in/hr), which is a theoretical lower bound on what could be measured with this approach and equipment. In practice, 0.5 in/hr appears to be a more realistic lower bound on the detectable erosion rate, based on inspection of computed changes and erosion rates.
Three flow speeds (5, 8, and 12 feet per second; ft/s) were targeted for the tests. Because of equipment limitations, it was not possible in the field to reach an average of 12 ft/s throughout any given borehole, although much higher flow speeds were reached locally in some cases. Most tests featured at least two different flow rates, and the borehole was typically surveyed at least twice for each condition, to allow averaging to reduce the influence of random diameter measurement errors. Errors arising from out-of-round boreholes appeared to be uncommon.
Briaud and others (2017) recommend stepped increases in the flow rate during a borehole test. This approach was taken during initial testing but proved to be problematic. The drilled hole would be enlarged by the first (smaller) discharge, and then it would be difficult to reach the desired higher flow speed because of the larger annulus between the drilled hole and the drilling rod that supplied the water for testing. This was largely solved by starting with a high discharge and, in many cases, maintaining it for subsequent tests with the average flow speed decreasing as the hole enlarged.
Several different measures of erosion rate were computed and investigated by comparison to lithological profiles. The vertically averaged erosion rate for each hole was computed, but this result does not reveal vertical variability of erodibility; and the mean flow speed within the hole is not a good representation of the speed when attempting to determine a relationship between erosion rate and flow speed. Instead, for each 6-inch layer within the hole, vertically averaged erosion rates and local flow speeds were computed and plotted. Where possible, the soil type for each layer was identified. For later laboratory analysis, project protocol dictated collection of Shelby tube samples whenever clay was encountered.
Plots of erosion rate versus flow speed displayed scatter that indicate that several other factors influence the erosion potential of the soil. Blow count was not a good predictor variable; it is better correlated with soil type than erodibility.
Soils were classified as sand, silt, or clay, depending on which soil type dominated within a sample. In general, those classified as sand and silt did not reveal clear patterns allowing erosion rate to be computed directly from flow speed, but the test results define the range and bounds on the erosion rate. Results for clay were slightly clearer with the erosion rate increasing with flow speed, once a threshold had been reached. In this case, the erosion rate appeared to change near a speed of 7 ft/s; above this threshold, erosion rates jumped from less than 2 in/hr to greater than 3 in/hr.
Even for soils with similar classifications, large differences in erodibility were observed between sites and in different layers within an individual hole. One potential means of dealing with this problem would be to perform more tests at each site to allow establishment of relationships between flow speed and erodibility for individual layers within a borehole. The maximum number of tests performed at a site in this study was four, but in some cases, results are available for only one or two flow events. Comparison of data to a set of Erosion Function Apparatus tests that provide better resolution of the vertical variation in the erosion rate versus flow speed relationship would allow further investigation of this idea.
It was hypothesized that drilling mud could expand the utility of the test in soft sands by reducing the likelihood of slumping that would be interpreted as erosion. The one test that was performed with drilling mud indicated that it greatly reduced the erosion rate of the soils encountered. It yielded very different results from the test performed at the same site with water.
Erosion rate is often expressed as a function of shear stress applied to a soil. In order to compute shear stress on the walls of the drilled hole, one must assume a form for the relationship between flow speed and shear stress and select a friction factor that is often estimated empirically from head loss, observed water-surface profiles, surface roughness, or other data not available in this report. One methodology for computing shear stress from flow speed is discussed in this report, but the test results have been presented in terms of erosion rate versus flow speed to avoid assuming values that are not verifiable via the field data collected in this study. Erosion rate was computed from directly measured values (sequential borehole profiles) and flow speed was computed directly from measured quantities (discharge and borehole geometry).
The BET has seen limited application, primarily in clayey soils, whereas most of the soils encountered in this study were primarily sand or silt. The objective of the BET is to determine the erodibility of in situ soil below the ground or riverbed surface. The BET is simple in principle and has the advantage of revealing erodibility of in situ sediments below the ground or riverbed surface; it appears to be very useful in clayey soils, based on previously published work, but is more difficult to apply in sandy soils where slumping and water losses within the hole during testing are more likely to occur. The BET did reveal a large variation in the results both laterally and vertically, even for the same soil-type classification. It is thus recommended that the results be applied considering these spatial variations rather than attempting to universally assign an erosion-rate relationship to a particular soil type. Results have been provided showing the results by site and by sediment classification (sand, silt, and clay), to allow either approach. Where possible, it is important to rely on site-specific results because the erosion-rate relationship for a given soil type varied by site.
Data collected during this project have been made publicly available online via the U.S. Geological Survey (USGS) Sciencebase database. The measured borehole profiles, discharge, lithology log sheets, and photos are available in the data release that accompanies this report (see Work and Livsey (2019) in the “Selected References” section for the appropriate link).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205063","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Work, P., and Livsey, D., 2020, Sediment lithology and borehole erosion testing, American and Sacramento Rivers, California: U.S. Geological Survey Scientific Investigations Report 2020–5063, 92 p., https://doi.org/10.3133/sir20205063.","productDescription":"Report: vii, 92 p.; Data Release","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-110364","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":376205,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5063/coverthb.jpg"},{"id":376206,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5063/sir20205063.pdf","text":"Report","size":"10 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":376207,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96MCT2Q","linkHelpText":"Borehole Erosion Test data, Lower American and Sacramento Rivers, California, 2019 (ver. 3.0, July 2020)"}],"country":"United States","state":"California","city":"Sacramento","otherGeospatial":"American River, Sacramento Rivers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.7010498046875,\n 38.38903340675905\n ],\n [\n -121.22589111328126,\n 38.38903340675905\n ],\n [\n -121.22589111328126,\n 38.70694605159386\n ],\n [\n -121.7010498046875,\n 38.70694605159386\n ],\n [\n -121.7010498046875,\n 38.38903340675905\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Predictions of post‐wildfire flooding and debris flows are needed, typically with short lead times. Measurements of soil‐hydraulic properties necessary for model parameterization are, however, seldom available. This study quantified soil‐hydraulic properties, soil‐water retention, and selected soil physical properties within the perimeter of the 2017 Thomas Fire in California. The Thomas Fire burn scar produced catastrophic debris flows in January 2018, highlighting the need for improved prediction capability. Soil‐hydraulic properties were also indirectly estimated using relations tied to soil‐water retention. These measurements and estimates are examined in the context of parameterizing post‐wildfire hydrologic models. Tension infiltrometer measurements showed significant decreases (p < .05) in field‐saturated hydraulic conductivity (Kfs) and sorptivity (S) in burned areas relative to unburned areas. Wildfire effects on soil water‐retention were dominated by significant decreases in saturated soil‐water content (θS). The van Genuchten parameters α, N, and residual water content did not show significant wildfire effects. The impacts of the wildfire on hydraulic and physical soil properties were greatest in the top 1 cm, emphasizing that measurements of post‐fire soil properties should focus on the near‐surface. Reductions in Kfs, θs, and soil‐water retention in burned soils were attributed to fire‐induced decreases in soil structure evidenced by increases in dry bulk density. Sorptivity reductions in burned soils were attributed to increases in soil‐water repellency. Rapid post‐fire assessments of flash flood and debris flow hazards using physically‐based hydrologic models are facilitated by similarities between Kfs, S, and the Green–Ampt wetting front potential (ψf) with measurements at other southern CA burned sites. We suggest that ratios of burned to unburned Kfs (0.37), S (0.36), and ψf (0.66) could be used to scale unburned values for model parameterization. Alternatively, typical burned values (Kfs = 20 mm hr−1; S = 6 mm hr−0.5; ψf = 1.6 mm) could be used for model parameterization.
Environmental DNA (eDNA) analysis offers a promising tool for rapid and early detection of aquatic plant invasive species, but currently suffers from substantial unknowns that limit its widespread use in monitoring programs. We conducted the first study to test the factors related to eDNA-based detectability of 2 invasive aquatic plants, Egeria densa and Myriophyllum spicatum, over extended periods of time. Specifically, we examined how plant growth stage and abundance relate to detection in semi-natural and natural conditions. We conducted a mesocosm experiment over a 10-wk period to assess changes in eDNA detection as a function of plant growth and changing biomass. We also sampled lakes with varying species abundances and resampled a subset of lakes to test temporal variability in detection.
We used multilevel occupancy modeling to determine factors associated with detection and generalized linear mixed effects modeling to assess important predictors of eDNA concentration. In mesocosm experiments, we found that detection was less reliable while plants were actively growing but improved as a function of increasing senescence. Plant abundance in tanks was a poor predictor of detection in water samples. These findings were supported by field sampling, which resulted in higher detections for E. densa during senescence periods and only weak or ambiguous relationships between eDNA and total plant abundance in lakes for both species. Within lakes, proximity to shallow photic zones and discrete plant patches were associated with increased detections and concentrations of eDNA. However, detection at the lake scale (based on 4 sampling stations) was typically successful only at the highest levels of plant abundance. Detection and concentrations of eDNA were consistently lower for M. spicatum than for E. densa in the mesocosm experiment and field sampling, suggesting that overall detectability of aquatic invasive plants varies by species.
Our results support sampling during senescence periods to improve detection, but generally low levels of detection and weak relationships with plant abundance indicate that substantial hurdles remains to implement eDNA analysis for early detection of, and rapid response to, aquatic invasive plants.
","language":"English","publisher":"University of Chicago Press","doi":"10.1086/710106","usgsCitation":"Kuehne, L.M., Ostberg, C.O., Chase, D.M., Duda, J.J., and Olden, J., 2020, Use of environmental DNA to detect the invasive aquatic plants Myriophyllum spicatum and Egeria densa in lakes: Freshwater Science, v. 39, no. 3, p. 521-533, https://doi.org/10.1086/710106.","productDescription":"13 p.","startPage":"521","endPage":"533","ipdsId":"IP-112200","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":456072,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/710106","text":"Publisher Index Page"},{"id":436884,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90BVKTO","text":"USGS data release","linkHelpText":"Detection of invasive aquatic plants Myriophyllum spicatum and Egeria densa in lakes using eDNA, field and mesocosm data"},{"id":378079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kuehne, Lauren M","contributorId":222591,"corporation":false,"usgs":false,"family":"Kuehne","given":"Lauren","email":"","middleInitial":"M","affiliations":[{"id":40565,"text":"School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington 98195","active":true,"usgs":false}],"preferred":false,"id":797768,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ostberg, Carl O. 0000-0003-1479-8458","orcid":"https://orcid.org/0000-0003-1479-8458","contributorId":220731,"corporation":false,"usgs":true,"family":"Ostberg","given":"Carl","middleInitial":"O.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797769,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chase, Dorothy M. 0000-0002-7759-2687","orcid":"https://orcid.org/0000-0002-7759-2687","contributorId":203926,"corporation":false,"usgs":true,"family":"Chase","given":"Dorothy","email":"","middleInitial":"M.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797770,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duda, Jeffrey J. 0000-0001-7431-8634 jduda@usgs.gov","orcid":"https://orcid.org/0000-0001-7431-8634","contributorId":148954,"corporation":false,"usgs":true,"family":"Duda","given":"Jeffrey","email":"jduda@usgs.gov","middleInitial":"J.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":797771,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olden, Julian D.","contributorId":202893,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":797772,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210954,"text":"fs20203035 - 2020 - International geoscience collaboration to support critical mineral discovery","interactions":[],"lastModifiedDate":"2020-08-05T18:42:59.552525","indexId":"fs20203035","displayToPublicDate":"2020-07-08T15:20:00","publicationYear":"2020","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":"2020-3035","displayTitle":"International Geoscience Collaboration to Support Critical Mineral Discovery","title":"International geoscience collaboration to support critical mineral discovery","docAbstract":"The importance of critical minerals and the need to expand and diversify critical mineral supply chains has been endorsed by the Federal governments of Australia, Canada, and the United States. The geoscience organizations of Geoscience Australia, the Geological Survey of Canada and the U.S. Geological Survey have created the Critical Minerals Mapping Initiative to build a diversified critical minerals industry in Australia, Canada, and the United States by developing a better understanding of known critical mineral resources, determining geologic controls on critical mineral distribution for deposits currently producing byproducts, identifying new sources of supply through critical mineral potential mapping and quantitative mineral assessments, and promoting critical mineral discovery in all three countries.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203035","collaboration":"Prepared in collaboration with Geoscience Australia and the Geological Survey of Canada","usgsCitation":"Kelley, K.D., 2020, International geoscience collaboration to support critical mineral discovery: U.S. Geological Survey Fact Sheet 2020–3035, 2 p., https://doi.org/10.3133/fs20203035.","productDescription":"2 p.","onlineOnly":"N","ipdsId":"IP-119151","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":376169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3035/fs20203035.pdf","text":"Report","size":"1.87 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3035"},{"id":376168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3035/coverthb.jpg"}],"country":"Australia, Canada, United States","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-964
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The U.S. Geological Survey, in cooperation with the South Carolina Department of Transportation, investigated the effects of stormwater runoff from bridge decks on stream water quality conditions in South Carolina. The investigation assessed 5 bridges in 3 physiographic provinces in South Carolina (Piedmont, Upper Coastal Plain, and Lower Coast Plain) that had a range of bridge, traffic, and hydrologic characteristics. The five selected South Carolina bridge sites (coincident with U.S. Geological Survey stations) and corresponding highways were Lynches River at Effingham (station 02132000; U.S. Highway 52), North Fork Edisto River at Orangeburg (station 02173500; U.S. Highway 301), Turkey Creek above Huger (station 02172035; South Carolina Highway 41), South Fork Edisto River near Denmark (station 02173000; U.S. Highway 321), and Fishing Creek at Highway 5 below York (station 021473415; South Carolina Highway 5). Bridge decks at the selected sites used open chutes, scuppers, and downspouts to drain stormwater directly into the receiving water at evenly spaced intervals.
Stream water, sediment, and biological samples were collected and analyzed for a variety of constituents to evaluate the stream conditions for this study. Five to six stream samples were collected at transects upstream and downstream from each selected bridge site using the equal-width-increment technique during observable stormwater runoff. Routine samples of the receiving waters were collected 12 to 14 times at the upstream transect during nonstorm conditions. Samples were analyzed for physical properties, suspended sediment, nutrients, major ions, trace metals, polycyclic aromatic hydrocarbons, and Escherichia coli. Bridge-deck sediment and streambed sediment at upstream and downstream transects were collected once at each bridge site and analyzed for metals and semivolatile organic compounds that include polycyclic aromatic hydrocarbons. Benthic macroinvertebrate community surveys were conducted once using Hester-Dendy multiplate artificial substrate samplers deployed at multiple upstream and downstream transects concurrently.
Statistical analysis of the water-quality data determined that stormwater runoff from bridges did not significantly degrade physical properties, nor nutrient, trace-metal, Escherichia coli, and suspended-sediment concentrations at the selected sites beyond the variability at the upstream transect (no bridge influence) during the study period. During storm sampling at the bridge sites, water-quality conditions were statistically similar upstream and downstream from each bridge, except for greater turbidity, total nitrogen, and total organic nitrogen plus ammonia concentrations found downstream from the bridge site on Fishing Creek; higher total chromium concentrations detected downstream from the bridge site on Turkey Creek; and increased Escherichia coli concentrations found downstream from the bridge site on the North Fork Edisto River. Total recoverable lead, cadmium, and copper concentrations were the only trace metals that periodically exceeded the South Carolina Department of Health and Environmental Control freshwater aquatic-life criteria at some bridge sites (lead, copper, and cadmium in Turkey Creek; cadmium and lead in Fishing Creek; lead in the South Fork Edisto River and Lynches River), but the exceedances occurred more frequently during routine sampling upstream from the bridge sites than during storm sampling at upstream and downstream transects. In general, stormwater runoff from the bridge decks did not seem to be the major source of metal enrichment in receiving waters during the study period. North Fork and South Fork Edisto Rivers and Turkey Creek had only one storm sample that exceeded South Carolina Department of Health and Environmental Control recreational criterion for Escherichia coli at both the upstream and downstream locations, while Fishing Creek had more frequent exceedances. Polycyclic aromatic hydrocarbons were detected infrequently in the stream samples.
In general, sediment trace-metal concentrations were below the threshold and probable effect concentration at all bridge sites, except for the chromium concentration (45.1 milligrams per kilogram) detected upstream from the bridge site on Fishing Creek that exceeded the threshold effect concentration of 43.4 milligrams per kilogram. Based on enrichment ratios less than 1.5, bridge-deck runoff did not seem to be affecting trace-metal accumulation in the streambed sediment downstream from the bridge sites, except for lead at the bridge site on the Lynches River and manganese at the bridge site on Fishing Creek.
Individual polycyclic aromatic compound concentrations and the sum of 18 compounds did not exceed any threshold and probable effect concentrations, indicating polycyclic aromatic hydrocarbon concentrations in the streambed sediment at downstream and upstream transects were not likely to affect the health of benthic macroinvertebrate communities. Although the cumulative polycyclic aromatic hydrocarbon concentrations in downstream sediment at the sites on Turkey and Fishing Creeks were well below the threshold effect concentration of 1,610 micrograms per kilogram, the 3- to 100-fold increase in downstream concentrations indicated a strong probability of a bridge-deck runoff source.
Overall, benthic macroinvertebrate community health downstream from the bridge sites did not seem to be affected by bridge-deck runoff based on several multivariate analyses that indicated statistically similar benthic macroinvertebrate communities at upstream and downstream transects. Of the five bridge sites in this study, the site on Turkey Creek seemed to have the least healthy benthic macroinvertebrate communities because of the lowest Ephemeroptera, Plecoptera, and Trichoptera spp. (mayflies, stoneflies, and caddisflies, respectively) taxa, species richness, and diversity; and the highest biotic indices, indicative of poorer ecological health, at upstream and downstream transects. This ecological finding was not unexpected because of seasonal periods of negligible flow when dissolved-oxygen concentrations fell below 4 milligrams per liter during the study period. Of the five bridge sites in this study, the site on the South Fork Edisto River seemed to have healthier benthic macroinvertebrate communities because of the greater mean Ephemeroptera, Plecoptera, and Trichoptera spp. taxa; and lower mean biotic indices at upstream and downstream transects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205046","collaboration":"Prepared in cooperation with South Carolina Department of Transportation","usgsCitation":"Journey, C.A., Petkewich, M.D., Conlon, K.J., Caldwell, A.W., Clark, J.M., Riley, J.W., and Bradley, P.M., 2020, Effects of stormwater runoff from selected bridge decks on conditions of water, sediment, and biological quality in receiving waters in South Carolina, 2013 to 2018: U.S. Geological Survey Scientific Investigations Report 2020–5046, 101 p., https://doi.org/10.3133/sir20205046.","productDescription":"xii, 101 p.","numberOfPages":"101","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-099513","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":376048,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046_appendixes.xlsx","text":"Appendixes 1-3","size":"312 KB","linkFileType":{"id":3,"text":"xlsx"}},{"id":376047,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046.pdf","text":"Report","size":"5.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5046"},{"id":376046,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FXSV2Y","text":"USGS data release","linkHelpText":"Water-, Sediment-, and Biological-Quality Data for Waters Receiving Runoff from Five Bridges in South Carolina, 2013 to 2018"},{"id":376045,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5046/coverthb.jpg"},{"id":376051,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5046/sir20205046_appendixes_csv.zip","text":"Appendixes 1-3 (CSV)","size":"34.5 KB","linkFileType":{"id":6,"text":"zip"}}],"contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
When animals select areas to occupy, decisions involve trade-offs between the fitness benefits of obtaining critical resources and minimizing costs of biotic and abiotic factors that constrain their use. These processes can be more dynamic and complex for species inhabiting desert environments, where highly variable spatial and temporal distribution of precipitation can create high intra- and inter-annual variability in forage conditions and water availability, and thermal constraints can differ significantly among seasons and diel periods. We examined resource selection in desert bighorn sheep (Ovis canadensis mexicana) in Cabeza Prieta National Wildlife Refuge, Arizona, USA, at multiple spatial and temporal scales to gain insight into how a desert mammal responds to variations in climatic conditions. We used resource selection functions to test topographic, forage, and environmental features among seasons and diel periods, and between non-drought and drought conditions at the population and home-range scale. When precipitation was average, sheep selected for topographic features that were beneficial for predator avoidance (i.e., escape terrain—steep, rugged areas with high visibility) and locations near perennial water. When drought occurred, they ranged further from preferred escape terrain and perennial water, perhaps seeking forage conditions suitable to meet their nutritional requirements. On early (April–June) and late (July–September) summer days, sheep selected for more northerly aspects and locations with lower solar radiation, and in some periods, selection for these cooler areas coincided with periods when forage covariates, proximity to perennial water, and several topographic features were uninformative in resource selection models. These choices may be necessary trade-offs, foregoing good escape terrain and foraging areas, and access to water, for improved thermoregulation. This study highlights the importance of identifying resource selection at variable spatial and temporal scales when investigating the interrelationship between species and their environment. It provides insight into the dynamics of resource selection in desert mammals, and how they respond to constraints imposed on them by their environment. This work can serve to inform strategies for managing and conserving species living in arid environments when faced with climate change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3175","usgsCitation":"Gedir, J.V., Cain, J.W., Swetnam, T., Krausman, P.R., and Morgart, J.R., 2020, Extreme drought and adaptive resource selection by a desert mammal: Ecosphere, v. 11, no. 7, e03175, 19 p., https://doi.org/10.1002/ecs2.3175.","productDescription":"e03175, 19 p.","ipdsId":"IP-109452","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":456075,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3175","text":"Publisher Index Page"},{"id":395633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Cabeza Prieta National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.96279907226562,\n 32.3822809650579\n ],\n [\n -112.97378540039062,\n 32.507445513754526\n ],\n [\n -113.13858032226562,\n 32.50860363229596\n ],\n [\n -113.14544677734375,\n 32.42402179265739\n ],\n [\n -113.66180419921875,\n 32.41590703229392\n ],\n [\n -113.75930786132811,\n 32.227904590766364\n ],\n [\n -113.51348876953125,\n 32.113985463263816\n ],\n [\n -113.40225219726562,\n 32.09071916431268\n ],\n [\n -113.29513549804688,\n 32.10351636222566\n ],\n [\n -113.27728271484374,\n 32.10467965495091\n ],\n [\n -113.21548461914062,\n 32.13724583390058\n ],\n [\n -113.14544677734375,\n 32.098863043145876\n ],\n [\n -113.08227539062499,\n 32.127942397192314\n ],\n [\n -113.08639526367188,\n 32.20582936513577\n ],\n [\n -112.994384765625,\n 32.20234331330286\n ],\n [\n -113.03146362304688,\n 32.287132632616384\n ],\n [\n -113.04519653320312,\n 32.288293580436644\n ],\n [\n -113.05755615234375,\n 32.36952297435149\n ],\n [\n -113.06716918945312,\n 32.377641904110355\n ],\n [\n -113.06442260742188,\n 32.397356268013105\n ],\n [\n -113.03695678710938,\n 32.397356268013105\n ],\n [\n -113.01223754882812,\n 32.38344069307763\n ],\n [\n -112.96279907226562,\n 32.3822809650579\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Gedir, Jay V.","contributorId":171735,"corporation":false,"usgs":false,"family":"Gedir","given":"Jay","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":833508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swetnam, Tyson","contributorId":213550,"corporation":false,"usgs":false,"family":"Swetnam","given":"Tyson","email":"","affiliations":[{"id":38787,"text":"University of Arizona , BIO5 Institute, Tucson, AZ 85719","active":true,"usgs":false}],"preferred":false,"id":833751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krausman, Paul R.","contributorId":31467,"corporation":false,"usgs":true,"family":"Krausman","given":"Paul","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":833509,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morgart, John R.","contributorId":10891,"corporation":false,"usgs":true,"family":"Morgart","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":833510,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211519,"text":"70211519 - 2020 - Conceptual model for the removal of cold-trapped H2O ice on the Mars northern seasonal springtime polar cap","interactions":[],"lastModifiedDate":"2020-07-31T13:10:16.119741","indexId":"70211519","displayToPublicDate":"2020-07-08T10:49:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Conceptual model for the removal of cold-trapped H2O ice on the Mars northern seasonal springtime polar cap","title":"Conceptual model for the removal of cold-trapped H2O ice on the Mars northern seasonal springtime polar cap","docAbstract":"The transport of H2O ice along the retreating north polar seasonal CO2 ice cap has previously been modeled and observed. Spectral observations show that H2O ice forms on the interior of the seasonal cap, while thermal observations show these regions to be consistent with CO2 ice. Prior to the sublimation of the seasonal CO2, the observed H2O ice deposits are diminished—and because H2O ice sublimation rates are extremely slow while in direct thermal contact with CO2 ice, an alternate removal process must be operating. We propose a model where the process of removing these H2O deposits starts with insolation‐induced basal sublimation of the underlying CO2 ice. This sublimed gas would “seep” upward and into the interface between the two ices, increasing pressure until the gas pressure fractures the cold‐trapped H2O ice. Small fragments would be suspended while larger fragments would be pushed aside, exposing the underlying CO2 ice.
Paleoseismic rupture histories provide spatiotemporal models of earthquake moment release needed to test numerical models and lengthen the instrumental catalog. We develop a model of the fewest and thus largest magnitude earthquakes permitted by paleoseismic data for the last 1,500 years on the southern San Andreas and San Jacinto Faults, California, USA. The largest geometric complexity appears to regulate the system: Only two ruptures break the San Gorgonio Pass region, followed by episodes of ruptures that could bridge the northern San Jacinto Fault and the San Andreas Fault. When tested against independent data on slip per event, the model produces comparable values indicating the end‐member model does not underpredict rupture rates. Rupture of >85% of the fault length in the historic period between 1800 and 1857 and the subsequent quiescence is similar to epochs of activity in the prehistoric model, suggesting that regional clustering of seismicity could be a trait of the system.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088532","usgsCitation":"Scharer, K., and Yule, D., 2020, A maximum rupture model for the southern San Andreas and San Jacinto Faults California, derived from paleoseismic earthquake ages: Observations and limitations: Geophysical Research Letters, v. 47, e2020GL088532, 11 p., https://doi.org/10.1029/2020GL088532.","productDescription":"e2020GL088532, 11 p.","ipdsId":"IP-119093","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456089,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl088532","text":"Publisher Index Page"},{"id":377818,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Andreas Fault, San Jacinto Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.39941406249999,\n 36.59788913307022\n ],\n [\n -120.80566406250001,\n 36.13787471840729\n ],\n [\n -116.89453125,\n 32.76880048488168\n ],\n [\n -115.1806640625,\n 32.80574473290688\n ],\n [\n -115.13671875,\n 33.8339199536547\n ],\n [\n -119.39941406249999,\n 36.59788913307022\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2020-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Scharer, Katherine M. 0000-0003-2811-2496","orcid":"https://orcid.org/0000-0003-2811-2496","contributorId":217361,"corporation":false,"usgs":true,"family":"Scharer","given":"Katherine M.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797255,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yule, Doug","contributorId":239568,"corporation":false,"usgs":false,"family":"Yule","given":"Doug","email":"","affiliations":[{"id":36305,"text":"CSU Northridge","active":true,"usgs":false}],"preferred":false,"id":797256,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243728,"text":"70243728 - 2020 - Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes","interactions":[],"lastModifiedDate":"2023-05-18T14:02:55.45746","indexId":"70243728","displayToPublicDate":"2020-07-08T08:55:58","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9533,"text":"Soil Research","active":true,"publicationSubtype":{"id":10}},"title":"Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes","docAbstract":"Soil erosion by water affects soil organic carbon (SOC) migration and distribution, which are important processes for defining ecosystem carbon sources and sinks. Little has been done to quantify soil carbon erosion in the three major basins in China, the Yangtze River, Yellow River and Pearl River Basins, which contain the most eroded areas. This research attempts to quantify the lateral movement of SOC based on spatial and temporal patterns of water erosion rates derived from an empirical Unit Stream Power Erosion Deposition Model (USPED) model. The water erosion rates simulated by the USPED model agreed reasonably with observations (R2 = 0.43, P < 0.01). We showed that regional water erosion ranged within 23.3–50 Mg ha–1 year–1 during 1992–2013, inducing the lateral redistribution of SOC caused by erosion in the range of 0.027–0.049 Mg C ha–1 year–1, and that caused by deposition of 0.0079–0.015 Mg C ha–1 year–1, in the three basins. The total eroded SOC was 0.006, 0.002 and 0.001 Pg year–1 in the Yangtze River, Yellow River and Pearl River Basins respectively. The net eroded SOC in the three basins was ~0.0075 Pg C year–1. Overall, the annual average redistributed SOC rate caused by erosion was greater than that caused by deposition, and the SOC loss in the Yangtze River Basin was greatest among the three basins. Our study suggests that considering both processes of erosion and deposition – as well as effects of topography, rainfall, land use types and their interactions – on these processes are important to understand SOC redistribution caused by water erosion.
","language":"English","publisher":"CSIRO Publishing","doi":"10.1071/SR19325","usgsCitation":"Yang, Y., Zhu, Q., Liu, J., Li, M., Yuan, M., Chen, H., Peng, C., and Yang, Z., 2020, Estimating soil organic carbon redistribution in three major river basins of China based on erosion processes: Soil Research, v. 58, no. 6, p. 540-550, https://doi.org/10.1071/SR19325.","productDescription":"11 p.","startPage":"540","endPage":"550","ipdsId":"IP-107196","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":417209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Yangtze River, Yellow River and Pearl River 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Yan 0000-0003-0858-7603","orcid":"https://orcid.org/0000-0003-0858-7603","contributorId":245232,"corporation":false,"usgs":false,"family":"Yang","given":"Yan","email":"","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":873092,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhu, Qiuan","contributorId":197933,"corporation":false,"usgs":false,"family":"Zhu","given":"Qiuan","email":"","affiliations":[{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false},{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false}],"preferred":false,"id":873093,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liu, Jinxun 0000-0003-0561-8988 jxliu@usgs.gov","orcid":"https://orcid.org/0000-0003-0561-8988","contributorId":3414,"corporation":false,"usgs":true,"family":"Liu","given":"Jinxun","email":"jxliu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873094,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Li, Mingxu","contributorId":305521,"corporation":false,"usgs":false,"family":"Li","given":"Mingxu","email":"","affiliations":[{"id":66236,"text":"Northwest A&F University, China","active":true,"usgs":false}],"preferred":false,"id":873098,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yuan, Minshu","contributorId":305515,"corporation":false,"usgs":false,"family":"Yuan","given":"Minshu","email":"","affiliations":[{"id":66236,"text":"Northwest A&F University, China","active":true,"usgs":false}],"preferred":false,"id":873095,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chen, Huai","contributorId":172942,"corporation":false,"usgs":false,"family":"Chen","given":"Huai","email":"","affiliations":[{"id":27125,"text":"State Key Lab of Soil Erosion and Dryland Framing, NW A&F Unv, Yangling, China","active":true,"usgs":false}],"preferred":false,"id":873096,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Peng, Changhui","contributorId":197932,"corporation":false,"usgs":false,"family":"Peng","given":"Changhui","email":"","affiliations":[{"id":6612,"text":"State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China","active":true,"usgs":false},{"id":6613,"text":"Center of CEF/ESCER, Department of Biological Science, University of Quebec at Montreal, Montreal H3C 3P8, Canada","active":true,"usgs":false}],"preferred":false,"id":873097,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yang, Zhenan","contributorId":305522,"corporation":false,"usgs":false,"family":"Yang","given":"Zhenan","email":"","affiliations":[{"id":66236,"text":"Northwest A&F University, China","active":true,"usgs":false}],"preferred":false,"id":873099,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210991,"text":"70210991 - 2020 - Segmentation and supercycles: A catalog of earthquake rupture patterns from the Sumatran Sunda Megathrust and other well-studied faults worldwide","interactions":[],"lastModifiedDate":"2020-07-10T13:47:57.191861","indexId":"70210991","displayToPublicDate":"2020-07-08T08:46:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Segmentation and supercycles: A catalog of earthquake rupture patterns from the Sumatran Sunda Megathrust and other well-studied faults worldwide","docAbstract":"After more than 100 years of earthquake research, earthquake forecasting, which relies on knowledge of past fault rupture patterns, has become the foundation for societal defense against seismic natural disasters. A concept that has come into focus more recently is that rupture segmentation and cyclicity can be complex, and that a characteristic earthquake model is too simple to adequately describe much of fault behavior. Nevertheless, recognizable patterns in earthquake recurrence emerge from long, high resolution, spatially distributed chronologies. Researchers now seek to discover the maximum, minimum, and typical rupture areas; the distribution, variability, and spatial applicability of recurrence intervals; and patterns of earthquake clustering in space and time. The term “supercycle” has been used to describe repeating longer periods of elastic strain accumulation and release that involve multiple fault ruptures. However, this term has become very broadly applied, lumping together several distinct phenomena that likely have disparate underlying causes. We divide earthquake cycle behavior into four major classes that have different implications for seismic hazard and fault mechanics: 1) quasi-periodic similar ruptures, 2) clustered similar ruptures, 3) clustered complementary ruptures/rupture cascades, and 4) superimposed cycles. “Segmentation” is likewise an ambiguous term; we identify “master segments” and “asperities” as defined by barriers to fault rupture. These barriers may be persistent (rarely or never traversed), frequent (occasionally traversed), or ephemeral (changing location from cycle to cycle). We compile a catalog of the historical and paleoseismic evidence that currently exists for each of these types of behavior on major well-studied faults worldwide. Due to the unique level of paleoseismic and paleogeodetic detail provided by the coral microatoll technique, the Sumatran Sunda megathrust provides one of the most complete records over multiple earthquake rupture cycles. Long historical records of earthquakes along the South American and Japanese subduction zones are also vital contributors to our catalog, along with additional data compiled from subduction zones in Cascadia, Alaska, and Middle America, as well as the North Anatolian and Dead Sea strike-slip faults in the Middle East. We find that persistent and frequent barriers, rupture cascades, superimposed cycles, and quasi-periodic similar ruptures are common features of most major faults. Clustered similar ruptures do not appear to be common, but broad overlap zones between neighboring segments do occur. Barrier regions accommodate slip through reduced interseismic coupling, slow slip events, and/or smaller more localized ruptures, and are frequently associated with structural features such as subducting seafloor relief or fault trace discontinuities. This catalog of observations provides a basis for exploring and modeling root causes of rupture segmentation and cycle behavior. We expect that researchers will recognize similar behavior styles on other major faults around the world.","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106390","usgsCitation":"Philibosian, B.E., and Meltzner, A.J., 2020, Segmentation and supercycles: A catalog of earthquake rupture patterns from the Sumatran Sunda Megathrust and other well-studied faults worldwide: Quaternary Science Reviews, v. 241, 106390, 43 p., https://doi.org/10.1016/j.quascirev.2020.106390.","productDescription":"106390, 43 p.","ipdsId":"IP-103767","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456092,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2020.106390","text":"Publisher Index Page"},{"id":376257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"241","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":792358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meltzner, Aron J.","contributorId":193419,"corporation":false,"usgs":false,"family":"Meltzner","given":"Aron","email":"","middleInitial":"J.","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false},{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":792359,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210998,"text":"70210998 - 2020 - Assessing nest attentiveness of Common Terns via video cameras and temperature loggers","interactions":[],"lastModifiedDate":"2020-07-10T13:34:21.228537","indexId":"70210998","displayToPublicDate":"2020-07-08T08:32:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5641,"text":"Avian Research","active":true,"publicationSubtype":{"id":10}},"title":"Assessing nest attentiveness of Common Terns via video cameras and temperature loggers","docAbstract":"While nest attentiveness plays a critical role in the reproductive success of avian species, little nest attentiveness data with high temporal resolution is available for many species. However, improvements in both video monitoring and temperature logging devices present an opportunity to improve our understanding of this aspect of avian behavior. To investigate nest attentiveness behaviors and evaluate the ability of these technologies to record the needed data we monitored 13 nests across two Common Tern (Sterna hirundo) breeding colonies with a paired video camera - temperature logger approach, while monitoring 63 additional nests with temperature loggers alone. We examined data respective to four times of day: Morning (civil dawn-11:59), Peak (12:00-16:00), Cooling (16:01-civil dusk), and Night (civil dusk-civil dawn). Our results show that while successful nests had mostly short duration off-bouts and maintained consistent nest attentiveness throughout the day, failed nests had dramatic reductions in nest attentiveness during the Cooling and Night periods (p < 0.05) with one colony experiencing repeated nocturnal abandonment due to predation pressure from a Great Horned Owl (Bubo virginianus). Incubation appeared to ameliorate ambient temperatures during Night, as nests were significantly warmer during Night when birds were on versus off the nest (p < 0.05). Meanwhile, off bouts during the Peak period occurred during higher ambient temperatures, perhaps due to adults leaving the nest during the hottest periods to perform belly soaking. Unfortunately, temperature logger data alone had limited ability to predict nest attentiveness status, with results highly dependent on time of day and bout duration. While our methods did not affect hatching success (p > 0.05) video-monitored nests did have significantly lower clutch sizes (p < 0.05). Despite the high-quality data recorded in this study, the logistical and potential biological complications reported suggest that careful planning is needed before these devices can be utilized.","language":"English","publisher":"Springer","doi":"10.1186/s40657-020-00208-7","usgsCitation":"Sullivan, J.D., Marban, P., Mullinax, J.M., Brinker, D.F., McGowan, P.C., Callahan, C., and Prosser, D., 2020, Assessing nest attentiveness of Common Terns via video cameras and temperature loggers: Avian Research, v. 11, 22, 18 p., https://doi.org/10.1186/s40657-020-00208-7.","productDescription":"22, 18 p.","ipdsId":"IP-110831","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":456096,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40657-020-00208-7","text":"Publisher Index Page"},{"id":436887,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M7S70D","text":"USGS data release","linkHelpText":"Assessing Nest Attentiveness of Common Terns (Sterna hirundo) via Video Cameras and Temperature Loggers"},{"id":376251,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2020-07-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Sullivan, Jeffery D.","contributorId":202910,"corporation":false,"usgs":false,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":792376,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Marban, Paul R.","contributorId":221168,"corporation":false,"usgs":false,"family":"Marban","given":"Paul R.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":792378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":792379,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brinker, David F.","contributorId":207103,"corporation":false,"usgs":false,"family":"Brinker","given":"David","email":"","middleInitial":"F.","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":792380,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McGowan, Petter C.","contributorId":228870,"corporation":false,"usgs":false,"family":"McGowan","given":"Petter","email":"","middleInitial":"C.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":792381,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Callahan, Carl C.","contributorId":217953,"corporation":false,"usgs":false,"family":"Callahan","given":"Carl C.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":792382,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":792377,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70210949,"text":"ofr20201068 - 2020 - Development of a two-stage life cycle model for Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, Washington","interactions":[],"lastModifiedDate":"2020-07-09T13:43:08.205679","indexId":"ofr20201068","displayToPublicDate":"2020-07-08T08:31:31","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1068","displayTitle":"Development of a Two-Stage Life Cycle Model for Oncorhynchus kisutch (Coho Salmon) in the Upper Cowlitz River Basin, Washington","title":"Development of a two-stage life cycle model for Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, Washington","docAbstract":"Recovery of salmon populations in the upper Cowlitz River Basin depends on trap-and-haul efforts owing to impassable dams. Therefore, successful recovery depends on the collection of out-migrating juvenile salmon at Cowlitz Falls Dam (CFD) for transport below downstream dams, as well as the collection of adults for transport upstream from the dams. Tacoma Power began downstream fish collection efforts at CFD in the mid-1990s and has been working consistently since then to improve collection efficiency to support self-sustaining salmon and steelhead (Onchorhynchus spp.) populations in the upper Cowlitz River Basin. Although much work has focused on estimating fish collection efficiency (FCE), there has been relatively little focus on modeling population dynamics to understand how fish collection efficiency and other factors drive production of both juvenile and adult salmon over their life cycle. As a first step towards understanding the factors affecting population dynamics of Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, we developed a statistical life cycle model using adult escapement and age structure data, juvenile collection data, and juvenile fish collection efficiency estimates. The goal of the statistical life cycle model is to estimate annual production and survival during two critical life-stage transitions: the freshwater production from escapement of adults upstream from CFD to collection of juveniles at CFD, and the juvenile-to-adult survival from the time of collection at the dam to the return of adults. To structure the life cycle model, we used the Ricker stock-recruitment model to estimate juvenile production from the number of parent spawners. This approach allowed us to account for density dependence at high spawner abundances while estimating annual productivity, defined as the number of juveniles produced per spawner at low spawner abundance. We then expressed productivity as a function two key variables affecting the number of juveniles collected and transported at CFD: (1) annual FCE, and (2) the annual number of days that spill occurred at CFD from September 1 to April 30.
Our key findings were as follows:
Additionally, by including FCE in the model, we estimated that the median pre-collection productivity, defined as the number of juveniles produced per spawner when FCE=1, was 108.4 juveniles per spawner. Because this two-stage life cycle model partitions factors that affect fish production in river compared to the ocean environment and fish life stages, the model estimates should help inform fishery managers about the overall role that fish collection at CFD may have on the recovery and sustainability of coho salmon populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201068","collaboration":"Prepared in cooperation with Tacoma Power","usgsCitation":"Plumb, J.M., and Perry, R.W., 2020, Development of a two-stage life cycle model for Oncorhynchus kisutch (coho salmon) in the upper Cowlitz River Basin, Washington: U.S. Geological Survey Open-File Report 2020–1068, 25 p., https://doi.org/10.3133/ofr20201068.","productDescription":"iv, 25 p.","onlineOnly":"Y","ipdsId":"IP-117483","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":376162,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1068/coverthb.jpg"},{"id":376163,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1068/ofr20201068.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1068"}],"country":"United States","state":"Washington","otherGeospatial":"Cowlitz River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.97821044921875,\n 46.09228143052647\n ],\n [\n -121.8548583984375,\n 46.09228143052647\n ],\n [\n -121.8548583984375,\n 46.70596917928676\n ],\n [\n -122.97821044921875,\n 46.70596917928676\n ],\n [\n -122.97821044921875,\n 46.09228143052647\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
While the physical processes governing groundwater flow are well understood, and the computational resources now exist for solving the governing equations in three dimensions over continental-scale domains, there remains substantial uncertainty about the subsurface distribution of the properties that control groundwater flow and transport for much of the contiguous United States (CONUS). The transmissivity of the shallow subsurface is a key parameter for the simulation of water table position, shallow groundwater flow, and base-flow discharge, but is not well-characterized at large regional to continental scales. We used a process-based inversion of CONUS-extent groundwater information to generate national data sets of (a) the transmissivity of the shallow groundwater system, (b) the depth to the water table, (c) groundwater discharge as base-flow, and (d) long-term average water content in the unsaturated zone. CONUS-extent coverage was developed in the form of 75 subdomain models, with the spatial distribution of long-term average transmissivity for each subdomain model calibrated against water-levels derived from U.S. Geological Survey (USGS) observation wells, NHDPlusV2 first-order perennial streams, and National Wetlands Inventory (NWI) freshwater wetlands. Estimated transmissivities were lower in the western CONUS than the eastern CONUS, and across the CONUS both transmissivity and depth to water correlate with recharge, elevation, and topographic slope. These generated data sets provide spatially distributed, long-term average estimates of subsurface properties and hydrological states that we anticipate will complement other environmental modeling efforts as explanatory variables, boundary conditions, or transport pathways.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR026724","usgsCitation":"Zell, W.O., and Sanford, W.E., 2020, Calibrated simulation of the long-term average surficial groundwater system and derived spatial distributions of its characteristics for the contiguous United States: Water Resources Research, v. 56, no. 8, e2019WR026724, 16 p.; Data Release, https://doi.org/10.1029/2019WR026724.","productDescription":"e2019WR026724, 16 p.; Data Release","ipdsId":"IP-117925","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":436888,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91LFFN1","text":"USGS data release","linkHelpText":"MODFLOW 6 models used to simulate the long-term average surficial groundwater system for the contiguous United States"},{"id":430520,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"contiguous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n 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0000-0002-8782-6627","orcid":"https://orcid.org/0000-0002-8782-6627","contributorId":339721,"corporation":false,"usgs":true,"family":"Zell","given":"Wesley","email":"","middleInitial":"O.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":904935,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanford, Ward E. 0000-0002-6624-0280 wsanford@usgs.gov","orcid":"https://orcid.org/0000-0002-6624-0280","contributorId":2268,"corporation":false,"usgs":true,"family":"Sanford","given":"Ward","email":"wsanford@usgs.gov","middleInitial":"E.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":904936,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70212531,"text":"70212531 - 2020 - Deep Learning as a tool to forecast hydrologic response for landslide-prone hillslopes","interactions":[],"lastModifiedDate":"2020-08-19T13:25:09.670826","indexId":"70212531","displayToPublicDate":"2020-07-08T08:19:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Deep Learning as a tool to forecast hydrologic response for landslide-prone hillslopes","docAbstract":"Empirical thresholds for landslide warning systems have benefitted from the incorporation of soil‐hydrologic monitoring data, but the mechanistic basis for their predictive capabilities is limited. Although physically based hydrologic models can accurately simulate changes in soil moisture and pore pressure that promote landslides, their utility is restricted by high computational costs and nonunique parameterization issues. We construct a deep learning model using soil moisture, pore pressure, and rainfall monitoring data acquired from landslide‐prone hillslopes in Oregon, USA, to predict the timing and magnitude of hydrologic response at multiple soil depths for 36‐hr intervals. We find that observation records as short as 6 months are sufficient for accurate predictions, and our model captures hydrologic response for high‐intensity rainfall events even when those storm types are excluded from model training. We conclude that machine learning can provide an accurate and computationally efficient alternative to empirical methods or physical modeling for landslide hazard warning.
Assessments of groundwater and surface water budgets at a large scale, such as the contiguous United States, often separately analyze the complex dynamics linking the surface and subsurface categories of water resources. These dynamics include recharge and groundwater contributions to streamflow. The time-varying simulation of these complex hydrologic dynamics, across large spatial and temporal scales, remains a scientific challenge due to the complexity of the processes and data availability. In this study, groundwater fluxes and surface hydrologic processes are simulated across the contiguous US for 1950-2010. The simulation estimates the monthly water budget components, such as groundwater recharge, surface runoff, and evapotranspiration; streamflow in major rivers is routed while accounting for groundwater exchange. Human impacts are included through groundwater pumping, and climate variability is included, including variability in precipitation, temperature and potential evapotranspiration. The simulated groundwater level and river discharge have strong correlation with USGS observation wells and streamflow gages, with R2 values of 0.992 and 0.946, respectively. The simulated evapotranspiration is compared with three other published estimation methods, showing that it is able to capture the magnitude and seasonality of evapotranspiration over the Mississippi River basin. As such, the model is able to reasonably simulate the surface and groundwater budgets over the US, allowing for questions of the relative importance of climate and human impacts to be explored in the future.
As the Arctic warms and wildfire occurrence increases, talik formation in permafrost regions is projected to expand and affect the cycling of water and carbon. Yet, few unified field and modeling studies have examined this process in detail, particularly in areas of continuous permafrost. We address this gap by presenting multimethod, multiseasonal geophysical measurements of permafrost and liquid‐water content that reveal substantial talik development in response to recent wildfire in continuous permafrost of boreal Alaska. Results from observation‐based cryohydrogeologic model simulations suggest that predisturbance subsurface conditions are key factors influencing thaw response to fire disturbance and air temperature warming. Our high‐resolution integrated study illustrates enhanced vulnerability of boreal continuous permafrost, with observed talik formation that exceeds coarse‐scale model projections by ~100 years even under the most extreme future emissions scenario. Results raise important scaling questions for representing extreme permafrost thaw phenomena of growing widespread importance in large‐scale predictive models.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL087565","usgsCitation":"Rey, D., Walvoord, M.A., Minsley, B.J., Ebel, B., Voss, C., and Singha, K., 2020, Wildfire-initiated talik development exceeds current thaw projections: Observations and models from Alaska's continuous permafrost zone: Geophysical Research Letters, v. 47, no. 15, e2020GL087565, 11 p., https://doi.org/10.1029/2020GL087565.","productDescription":"e2020GL087565, 11 p.","ipdsId":"IP-116894","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":456104,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl087565","text":"Publisher Index Page"},{"id":381213,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Northeast Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -150.82031249999997,\n 64.77412531292873\n ],\n [\n -140.9765625,\n 64.77412531292873\n ],\n [\n -140.9765625,\n 70.37785394109224\n ],\n [\n -150.82031249999997,\n 70.37785394109224\n ],\n [\n -150.82031249999997,\n 64.77412531292873\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"15","noUsgsAuthors":false,"publicationDate":"2020-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Rey, David M. 0000-0003-2629-365X","orcid":"https://orcid.org/0000-0003-2629-365X","contributorId":211848,"corporation":false,"usgs":true,"family":"Rey","given":"David M.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806696,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walvoord, Michelle A. 0000-0003-4269-8366","orcid":"https://orcid.org/0000-0003-4269-8366","contributorId":211843,"corporation":false,"usgs":true,"family":"Walvoord","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806697,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":806698,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ebel, Brian A. 0000-0002-5413-3963","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":211845,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806699,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Voss, Clifford I. 0000-0001-5923-2752","orcid":"https://orcid.org/0000-0001-5923-2752","contributorId":211844,"corporation":false,"usgs":true,"family":"Voss","given":"Clifford I.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":806700,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Singha, Kamini 0000-0002-0605-3774","orcid":"https://orcid.org/0000-0002-0605-3774","contributorId":191366,"corporation":false,"usgs":false,"family":"Singha","given":"Kamini","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":806701,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211281,"text":"70211281 - 2020 - A national-scale assessment of mercury bioaccumulation in United States National Parks using dragonfly larvae as biosentinels through a citizen-science framework","interactions":[],"lastModifiedDate":"2020-07-22T15:35:59.640558","indexId":"70211281","displayToPublicDate":"2020-07-07T10:31:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"A national-scale assessment of mercury bioaccumulation in United States National Parks using dragonfly larvae as biosentinels through a citizen-science framework","docAbstract":"We conducted a national-scale assessment of mercury (Hg) bioaccumulation in aquatic ecosystems using dragonfly larvae as biosentinels, by developing a citizen science network to facilitate biological sampling. Implementing a carefully designed sampling methodology for citizen scientists, we developed an effective framework for landscape-level inquiry that might otherwise be resource limited. We assessed variation in dragonfly Hg concentrations across >450 sites spanning 100 US National Park Service units, and examined intrinsic and extrinsic factors associated with variation in Hg concentrations. Mercury concentrations ranged between 10.4-1,411 ng/g dry weight across sites and varied among habitat types. Dragonfly total Hg (THg) concentrations were up to 1.8-fold higher in lotic habitats than in lentic habitats, and 37% higher in waterbodies with abundant wetlands along their margins than those without wetlands. Mercury concentrations in dragonflies differed among families, but were correlated (R2>0.80) with each other, enabling adjustment to a consistent family to facilitate spatial comparisons among sampling units. Dragonfly THg concentrations were positively correlated with THg in both fish and amphibians from the same locations, indicating that dragonfly larvae are effective indicators of Hg bioavailability in aquatic food webs. Collectively, this continental-scale study demonstrates the utility of dragonfly larvae for estimating potential mercury risk to fish and wildlife in aquatic ecosystems and provides a framework for engaging citizen science as a component of landscape Hg monitoring programs.","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.0c01255","usgsCitation":"Eagles-Smith, C., Willacker, J., Nelson, S.J., Flanagan Pritz, C.M., Krabbenhoft, D.P., Chen, C.Y., Ackerman, J., Campbell Grant, E.H., and Pilliod, D.S., 2020, A national-scale assessment of mercury bioaccumulation in United States National Parks using dragonfly larvae as biosentinels through a citizen-science framework: Environmental Science and Technology, v. 54, no. 14, p. 8779-8790, https://doi.org/10.1021/acs.est.0c01255.","productDescription":"12 p.","startPage":"8779","endPage":"8790","ipdsId":"IP-117106","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science 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