We investigate bias in ground motions predicted for Central and Eastern North America (CENA) using ground motion models (GMMs) combined with site amplification models developed in the NGA-East project. Bias is anticipated because of de-coupled procedures used in the development of the GMMs and site amplification models. The NGA-East GMMs were mainly calibrated by adjusting CENA data to a reference site condition using a site amplification model appropriate for active tectonic regions. Hence, these GMMs are likely biased relative to the CENA reference site condition (3000 m/sec shear wave velocity). Moreover, the NGA-East site amplification model recommended for hazard applications contains a simulation-based term for amplification between the reference condition and time-averaged shear wave velocity VS30=760 m/sec, which is uncertain and has not been calibrated against data from sites with that reference condition. Using the NGA-East dataset, we apply mixed-effects residual analysis and identify that period-dependent bias in 5% damped response spectral acceleration is present across a wide frequency range, but is strongest (i.e., overestimating by a factor of 2) at short oscillator periods <0.2 sec. Ongoing work to remedy this bias consists of expanding the NGA-East dataset with more recent recordings and enhanced metadata, particularly regarding site conditions.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings from the 12th national conference on earthquake engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"12th National Conference on Earthquake Engineering","conferenceDate":"Jun 27 - Jul 1, 2022","conferenceLocation":"Salt Lake City, UT","language":"English","publisher":"Earthquake Engineering Research Institute","usgsCitation":"Ramos-Sepulveda, M.E., Parker, G.A., Li, M., Ilhan, O., Hashash, Y.M., Rathje, E., and Stewart, J.P., 2022, Performance of NGA-East GMMs and site amplification models relative to CENA ground motions, in Proceedings from the 12th national conference on earthquake engineering, Salt Lake City, UT, Jun 27 - Jul 1, 2022, 4 p.","productDescription":"4 p.","ipdsId":"IP-134994","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":411886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":411885,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://12ncee.org/program/proceedings"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ramos-Sepulveda, Maria E.","contributorId":294748,"corporation":false,"usgs":false,"family":"Ramos-Sepulveda","given":"Maria","email":"","middleInitial":"E.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":848818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parker, Grace Alexandra 0000-0002-9445-2571","orcid":"https://orcid.org/0000-0002-9445-2571","contributorId":237091,"corporation":false,"usgs":true,"family":"Parker","given":"Grace","email":"","middleInitial":"Alexandra","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":848819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Meibai","contributorId":294749,"corporation":false,"usgs":false,"family":"Li","given":"Meibai","email":"","affiliations":[{"id":34217,"text":"UT Austin","active":true,"usgs":false}],"preferred":false,"id":848820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ilhan, Okan","contributorId":294751,"corporation":false,"usgs":false,"family":"Ilhan","given":"Okan","email":"","affiliations":[{"id":63637,"text":"Ankara Bildirim Beyazıt University, Turkey","active":true,"usgs":false}],"preferred":false,"id":848821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hashash, Youssef M. A.","contributorId":294752,"corporation":false,"usgs":false,"family":"Hashash","given":"Youssef","email":"","middleInitial":"M. 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In developing ground motion models for response spectra, generally, the damping of the oscillator is set to a reference value of five percent of the critical damping. Damping scaling factors (DSF) are used to translate the predictions of 5%-damped ground motion models to any damping ratio of interest. Rezaeian et al. (2014) developed a DSF model based on the NGA-West2 database, capturing the effect of oscillator period, event magnitude, and source-to-site distance. This paper investigates the applicability of that model for the horizontal component of ground motions recorded in France, using Traversa et al. (2020) database for the analysis.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings from the 12th national conference on earthquake engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"12th National Conference on Earthquake Engineering","conferenceDate":"Jun 27 - Jul 1, 2022","conferenceLocation":"Salt Lake City, UT","language":"English","publisher":"Earthquake Engineering Research Institute","usgsCitation":"Bahrampouri, M., Rezaeian, S., Traversa, P., Al Atik, L., Mazzoni, S., and Bozorgnia, Y., 2022, Applicability of the NGA-West2 damping scaling factors to ground motions recorded in France, in Proceedings from the 12th national conference on earthquake engineering, Salt Lake City, UT, Jun 27 - Jul 1, 2022, 5 p.","productDescription":"5 p.","ipdsId":"IP-136059","costCenters":[{"id":300,"text":"Geologic Hazards Science 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L.","contributorId":270335,"corporation":false,"usgs":false,"family":"Al Atik","given":"L.","affiliations":[{"id":56147,"text":"Linda Alatik Consulting, San Francisco, CA 94110","active":true,"usgs":false}],"preferred":false,"id":849420,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mazzoni, S.","contributorId":270337,"corporation":false,"usgs":false,"family":"Mazzoni","given":"S.","affiliations":[{"id":56148,"text":"University of California, Los Angeles, CA 90095","active":true,"usgs":false}],"preferred":false,"id":849421,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bozorgnia, Y.","contributorId":203475,"corporation":false,"usgs":false,"family":"Bozorgnia","given":"Y.","affiliations":[{"id":36629,"text":"University of California","active":true,"usgs":false}],"preferred":false,"id":849422,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236825,"text":"dr1162 - 2022 - Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2021","interactions":[],"lastModifiedDate":"2026-03-18T19:31:00.697785","indexId":"dr1162","displayToPublicDate":"2022-09-20T10:17:13","publicationYear":"2022","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":"1162","displayTitle":"Water-Level Data for the Albuquerque Basin and Adjacent Areas, Central New Mexico, Period of Record Through September 30, 2021","title":"Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2021","docAbstract":"The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained primarily from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of water year 2021, the network consisted of 120 wells and piezometers at 54 locations. The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels at the wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at the sites through water year 2021 (October 1, 2020, through September 30, 2021). Water-level data that were collected in previous water years from wells that were later discontinued were published in previous USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1162","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Bell, M.T., and Montero, N.Y., 2022, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2021: U.S. Geological Survey Data Report 1162, 43 p., https://doi.org/10.3133/dr1162.","productDescription":"Report: iv, 43 p.; Dataset","numberOfPages":"52","onlineOnly":"Y","ipdsId":"IP-138357","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":406961,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1162/coverthb.jpg"},{"id":501270,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113524.htm","linkFileType":{"id":5,"text":"html"}},{"id":407061,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/dr1162/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":406967,"rank":5,"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":406966,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1162/images"},{"id":406965,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1162/dr1162.XML"},{"id":406963,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1162/dr1162.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1162"}],"country":"United States","state":"New Mexico","otherGeospatial":"Albuquerque Basin and adjacent areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.2540283203125,\n 33.95247360616282\n ],\n [\n -106.248779296875,\n 33.95247360616282\n ],\n [\n -106.248779296875,\n 35.51434313431818\n ],\n [\n -107.2540283203125,\n 35.51434313431818\n ],\n [\n -107.2540283203125,\n 33.95247360616282\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Investigators have speculated that the climate-driven “greening of the Arctic” may benefit barren-ground caribou populations, but paradoxically many populations have declined in recent years. This pattern has raised concerns about the influence of summer habitat conditions on caribou demographic rates, and how populations may be impacted in the future. The short Arctic summer provides caribou with important forage resources but is also the time they are exposed to intense harassment by insects, factors which are both being altered by longer, warmer growing seasons. To better understand the effects of summer forage and insect activity on Arctic caribou demographic rates, we investigated the influence of estimated forage biomass, digestible energy (DE), digestible nitrogen (DN), and mosquito activity on the reproductive success and survival of adult females in the Central Arctic Herd on the North Slope of Alaska. We tested the hypotheses that greater early summer DN would increase subsequent reproduction (parturition and late June calving success) while greater biomass and DE would increase adult survival (September–May), and that elevated mosquito activity would reduce both demographic rates. Because the period when abundant forage DN is limited and overlaps with the period of mosquito harassment, we also expected years with low DN and high harassment to synergistically reduce caribou reproductive success. Examining these relationships at the individual-level, using GPS-collared females, and at the population-level, using long-term monitoring data, we generally found support for our expectations. Greater early summer DN was associated with increased subsequent calving success, while greater summer biomass was associated with increased adult survival. Mosquito activity was associated with reductions in adult female parturition, late June calving success, and survival, and in years with low DN, had compounding effects on subsequent late June calving success. Our findings indicate that summer nutrition and mosquito activity collectively influence the demographic rates of Arctic caribou, and may impact the dynamics of populations in the future under changing environmental conditions.
This report describes geodetic and geologic information used to constrain deformation models of the 2023 update to the National Seismic Hazard Model (NSHM), a set of deformation models to interpret these data, and their implications for earthquake rates in the western United States. Recent updates provide a much larger data set of Global Positioning System crustal velocities than used in the 2014 NSHM, as well as hundreds of new faults considered as active sources for the 2023 NSHM. These data are interpreted by four geodetic models of deformation that estimate fault slip rates and their uncertainties together with off‐fault moment release rates. Key innovations in the 2023 NSHM relative to past practice include (1) the addition of two new (in addition to two existing) deformation models, (2) the revision and expansion of the geologic slip rate database, (3) accounting for fault creep through development of a creep‐rate model that is employed by the four deformation models, and (4) accounting for time‐dependent earthquake‐cycle effects through development of viscoelastic models of the earthquake cycle along the San Andreas fault and the Cascadia subduction zone. The effort includes development of a geologic deformation model that complements the four geodetic models. The current deformation models provide a new assessment of outstanding discrepancies between geologic and geodetic slip rates, at the same time highlighting the need for both geologic and geodetic slip rates to robustly inform the earthquake rate model.
Global Positioning System (GPS) velocity solutions of the western United States (WUS) are compiled from several sources of field networks and data processing centers for the 2023 U.S. Geological Survey National Seismic Hazard Model (NSHM). These solutions include both survey and continuous‐mode GPS velocity measurements. I follow the data processing procedure of Parsons et al. (2013) for the Uniform California Earthquake Rupture Forecast, version 3 and McCaffrey, Bird, et al. (2013) and Zeng and Shen (2013) for their WUS deformation models in support of the 2014 NSHM update. All GPS velocity vectors are first rotated to a common North American reference frame. I edit the velocities to remove outliers and data with significant influence from volcanism. The solutions are then combined into a final GPS velocity field consisting of 4979 horizontal velocity vectors. I compute strain rates based on these GPS velocities using the method of Shen et al. (2015). These strain rates correlate closely with seismicity rates in the WUS. The results are used for WUS geodetic and geologic deformation modeling in support of the 2023 NSHM update.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220180","usgsCitation":"Zeng, Y., 2022, GPS velocity field of the Western United States for the 2023 National Seismic Hazard Model update: Seismological Research Letters, v. 93, no. 6, p. 3121-3134, https://doi.org/10.1785/0220220180.","productDescription":"14 p.","startPage":"3121","endPage":"3134","ipdsId":"IP-142151","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":407046,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.94726562499999,\n 28.459033019728043\n ],\n [\n -103.0078125,\n 28.459033019728043\n ],\n [\n -103.0078125,\n 49.61070993807422\n ],\n [\n -125.94726562499999,\n 49.61070993807422\n ],\n [\n -125.94726562499999,\n 28.459033019728043\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"93","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-09-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Zeng, Yuehua 0000-0003-1161-1264 zeng@usgs.gov","orcid":"https://orcid.org/0000-0003-1161-1264","contributorId":145693,"corporation":false,"usgs":true,"family":"Zeng","given":"Yuehua","email":"zeng@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":852329,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70242153,"text":"70242153 - 2022 - Western U.S. geologic deformation model for use in the U.S. National Seismic Hazard Model 2023","interactions":[],"lastModifiedDate":"2023-04-10T11:52:49.463107","indexId":"70242153","displayToPublicDate":"2022-09-19T06:48:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Western U.S. geologic deformation model for use in the U.S. National Seismic Hazard Model 2023","docAbstract":"Fault geometry and slip rates are key input data for geologic deformation models, which are a fundamental component of probabilistic seismic hazard analyses (PSHAs). However, geologic sources for PSHA have traditionally been limited to faults with field‐based slip rate constraints, which results in underrepresentation of known, but partially characterized, active faults. Here, we evaluate fault geometries and geologic fault slip rates for the western United States to construct a new geologic deformation model for the U.S. National Seismic Hazard Model 2023 update (NSHM23). In previous NSHM iterations, only faults with published geologic slip rates were included. In the NSHM23 fault sections database compilation, this inclusion criterion was expanded to include faults without known slip rates. In this updated geologic deformation model, preferred slip rates and associated uncertainty distributions are incorporated for faults with slip rates derived from field studies. For faults without site‐specific slip rates, we evaluate a suite of uncertainty distributions derived from broad slip rate categories in the U.S. Geological Survey Quaternary Fault and Fold Database. Preferred slip rate distributions are selected via comparison with geodetic strain rates in tectonic subregions. The resultant moment of the geologic deformation model is generally in deficit compared with the geodetic moment within each region. Primary advances in the NSHM23 geologic deformation model include the following: (1) slip rates are presented as preferred values with uncertainties rather than single values; (2) the representation of the western U.S. active fault network is more complete; and (3) the geologic deformation model leverages geodetic information to assess regional constraints on geologic fault slip rates.
This study presents Raman spectroscopic data paired with scanning electron microscopy (SEM) images to assess solid bitumen composition as a function of solid bitumen texture and association with minerals. A series of hydrous pyrolysis experiments (1–103 days, 300–370 °C) using a low maturity (0.25% solid bitumen reflectance, BRo), high total organic carbon [(TOC), 14.0 wt%] New Albany Shale sample as the starting material yielded pyrolysis residues designed to evaluate the evolution of solid bitumen aromaticity with increasing temperature and heating duration. Solid bitumen was analyzed by Raman spectroscopy wherein point data were collected from accumulations that ranged in size and degree of association with the mineral matrix. Raman spectroscopy results show that with increasing temperature and experimental duration, solid bitumen aromaticity increases and compositional variability decreases. With regards to texture and composition, coarser-grained solid bitumen (>1.3 μm from nearest mineral grain) has consistently higher, but less variable aromaticity than thinner, wispy solid bitumen which is more intimately associated with the mineral matrix. Collocated scanning electron microscope images were used to provide qualitative assessments of porosity hosted by the organic matter. These paired data sets suggest that solid bitumen porosity development and molecular composition are linked, and these parameters are related to the textural relationships of the organic matter within the whole rock. These results are discussed with perspective towards understanding how rock fabric and texture can influence organic matter evolution during thermal maturation of organic-rich marine shales and inform our broader understanding of these important energy resources.
The Loch Vale Watershed Research and Monitoring Program collects long-term datasets of ecological and biogeochemical parameters in Rocky Mountain National Park to support both (1) management of this protected area and (2) research into watershed-scale ecosystem processes as those processes respond to atmospheric deposition and climate variability. The program collects data on precipitation depth and atmospheric deposition chemistry—as well as surface water biogeochemistry—within the watershed and in other areas of the park. These data are used by resource managers, scientists, policy makers, and students, so it is important that all collected data meet high quality standards. This report presents an evaluation of data quality for precipitation, atmospheric ammonia, and surface water quality samples collected from 2010 to 2019. This report also presents changes made to the monitoring and laboratory equipment used during the study period and describes new data streams added to the project, including atmospheric ammonia, surface water chlorophyll-a, and dissolved oxygen in two lakes: The Loch and Sky Pond.
Quality-assurance procedures looked at the accuracy and precision of measurements made over the study period and found that precipitation and surface water chemistry data were 99 percent accurate and precise. Records that failed to meet quality standards were removed from published databases. From 2010 to 2014, a colocated precipitation gauge and deposition collector were installed on site as quality checks. From 2014 to 2018, power loss at the site resulted in significant loss of precipitation data records during the snow seasons. Those problems were addressed by installing new solar-power equipment in 2019. Measurements of deposition chemistry, atmospheric ammonia deposition, and surface water biogeochemistry were all sufficiently complete and consistent to support project data needs.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm1D9","usgsCitation":"Weinmann, T., Baron, J.S., and Jayo, A., 2022, Quality assurance report for Loch Vale Watershed, 2010–19: U.S. Geological Survey Techniques and Methods 1–D9, 21 p., https://doi.org/10.3133/tm1D9.","productDescription":"Report: viii, 21 p.; Data Release; Database","onlineOnly":"Y","ipdsId":"IP-127394","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":406494,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/01/d9/coverthb.jpg"},{"id":406495,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/01/d9/tm1d9.pdf","text":"Report","size":"2.82 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T and M 1-D9"},{"id":406498,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92ULNAG","text":"USGS data release","linkHelpText":"Climatological data for the Loch Vale watershed in Rocky Mountain National Park, Colorado, water years 1992–2019"},{"id":406738,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/tm/01/d9/images"},{"id":406739,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/tm/01/d9/tm1d9.xml"},{"id":406741,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://nadp.slh.wisc.edu/","text":"National Atmospheric Deposition Program [NADP], 2021, National Atmospheric Deposition Program web page—","linkHelpText":"accessed August 17, 2021"},{"id":406742,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://www2.nrel.colostate.edu/projects/lvws/index.html","text":"National Resource Ecology Lab [NREL], 2011, Loch Vale Watershed—Long-term Ecological Research and Monitoring Program: National Resource Ecology Laboratory web page—","linkHelpText":"accessed April 15, 2021"},{"id":406744,"rank":9,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—","linkHelpText":"U.S. Geological Survey National Water Information System database, accessed July 28, 2021"},{"id":406743,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://co.water.usgs.gov/lochvale/","text":"Water, Energy, and Biochemical Budgets (WEBB)—Loch Vale Watershed: Colorado Water Science Center web page—","linkHelpText":"accessed July 13, 2021"}],"country":"United States","state":"Colorado","otherGeospatial":"Loch Vale Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.292724609375,\n 39.605688178320804\n ],\n [\n -105.01281738281249,\n 39.605688178320804\n ],\n [\n -105.01281738281249,\n 40.56806745430726\n ],\n [\n -106.292724609375,\n 40.56806745430726\n ],\n [\n -106.292724609375,\n 39.605688178320804\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
This report summarizes data-collection activities associated with the U.S. Geological Survey Humboldt Bay Water-Quality and Salt Marsh Monitoring Project. This work was undertaken to gain a comprehensive understanding of water-quality conditions, salt marsh accretion processes, marsh-edge erosion, and soil-carbon storage in Humboldt Bay, California. Multiparameter sondes recorded water temperature, specific conductance, and turbidity at a 15-minute timestep at two U.S. Geological Survey water-quality stations: (1) Mad River Slough near Arcata, California (U.S. Geological Survey station 405219124085601) and (2) Hookton Slough near Loleta, California (U.S. Geological Survey station 404038124131801). At each station, discrete water samples were collected to develop surrogate regression models that were used to compute a continuous time series of suspended-sediment concentration from continuously measured turbidity. Data loggers recorded water depth at a 6-minute timestep in the primary tidal channels (Mad River Slough and Hookton Slough) in two adjacent marshes (Mad River marsh and Hookton marsh). The marsh monitoring network included five study marshes. Three marshes (Mad River, Manila, and Jacoby) are in the northern embayment of Humboldt Bay and two marshes (White and Hookton) are in the southern embayment. Surface deposition and elevation change were measured using deep rod surface elevation tables and feldspar marker horizons. Sediment characteristics and soil-carbon storage were measured using a total of 10 shallow cores, distributed across 5 study marshes, collected using an Eijkelkamp peat sampler. Rates of marsh edge erosion (2010–19) were quantified in four marshes (Mad River, Manila, Jacoby, and White) by estimating changes in the areal extent of the vegetated marsh plain using repeat aerial imagery and light detection and ranging (LiDAR)-derived elevation data. During the monitoring period (2016–19), the mean suspended-sediment concentration computed for Hookton Slough (50±20 milligrams per liter [mg/L]) was higher than Mad River Slough (18±7 mg/L). Uncertainty in mean suspended-sediment concentration values is reported using a 90-percent confidence interval. Across the five study marshes, elevation change (+1.8±0.6 millimeters per year [mm/yr]) and surface deposition (+2.5±0.5 mm/yr) were lower than published values of local sea-level rise (4.9±0.8 mm/yr), and mean carbon density was 0.029±0.005 grams of carbon per cubic centimeter. From 2010 to 2019, marsh edge erosion and soil carbon loss were greatest in low-elevation marshes with the marsh edge characterized by a gentle transition from mudflat to vegetated marsh (herein, ramped edge morphology) and larger wind-wave exposure. Jacoby Creek marsh experienced the greatest edge erosion. In total, marsh edge erosion was responsible for 62.3 metric tons of estuarine soil carbon storage loss across four study marshes. Salt marshes are an important component of coastal carbon, which is frequently referred to as “blue carbon.” The monitoring data presented in this report provide fundamental information needed to manage blue carbon stocks, assess marsh vulnerability, inform sea-level rise adaptation planning, and build coastal resiliency to climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221076","collaboration":"Prepared in cooperation with the California State Coastal Conservancy, California Department of Fish and Wildlife, and U.S. Fish and Wildlife Service—Humboldt Bay National Wildlife Refuge","usgsCitation":"Curtis, J.A., Thorne, K.M., Freeman, C.M., Buffington, K.J., and Drexler, J.Z., 2022, A summary of water-quality and salt marsh monitoring, Humboldt Bay, California: U.S. Geological Survey Open-File Report 2022–1076, 30 p., https://doi.org/10.3133/ofr20221076.","productDescription":"Report: viii, 30 p.; 3 Data Releases","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-133425","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":501826,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113519.htm","linkFileType":{"id":5,"text":"html"}},{"id":406874,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221076/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":406865,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TVX0Z8","text":"Model archive summary for a suspended-sediment concentration surrogate regression model for station 405219124085601; Mad River Slough near Arcata, CA","description":"Curtis, J.A., 2021b, Model archive summary for a suspended-sediment concentration surrogate regression model for station 405219124085601; Mad River Slough near Arcata, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9TVX0Z8."},{"id":406863,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1076/images"},{"id":406864,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RJTAIL","text":"Model archive summary for a suspended-sediment concentration surrogate regression model for station 404038124131801; Hookton Slough near Loleta, CA","description":"Curtis, J.A., 2021a, Model archive summary for a suspended-sediment concentration surrogate regression model for station 404038124131801; Hookton Slough near Loleta, CA: U.S. Geological Survey data release, https://doi.org/10.5066/P9RJTAIL."},{"id":406866,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QLAL7B","text":"Salt marsh monitoring during water years 2013 to 2019, Humboldt Bay, CA—Water levels, surface deposition, elevation change, and soil carbon storage","description":"Curtis, J.A., Thorne, K.M., Freeman, C.M., Buffington, K.J., and Drexler, J.Z., 2022, Salt marsh monitoring during water years 2013 to 2019, Humboldt Bay, CA—Water levels, surface deposition, elevation change, and soil carbon storage: U.S. Geological Survey data release, https://doi.org/10.5066/P9QLAL7B."},{"id":406860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1076/covrthb.jpg"},{"id":406861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1076/ofr20221076.pdf","text":"Report","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":406862,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1076/ofr20221076.xml"}],"country":"United States","state":"California","otherGeospatial":"Humboldt Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.35699462890624,\n 40.55972134684838\n ],\n [\n -124.0191650390625,\n 40.55972134684838\n ],\n [\n -124.0191650390625,\n 40.97678774053031\n ],\n [\n -124.35699462890624,\n 40.97678774053031\n ],\n [\n -124.35699462890624,\n 40.55972134684838\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
Preliminary models for gate operations at the new outlet control structure for Reelfoot Lake were developed by the U.S. Geological Survey, using calibrated ratings of the lift gates, to support the U.S. Fish and Wildlife Service in managing lake level. In 2018, the old structure at the outlet of Reelfoot Lake was buried and lake level control was transferred to a new structure. The transition from lake-level management of the old control structure to the new control structure was documented using historical lake level and discharge measurements and records of stop-log management from March 7, 2013, to August 12, 2018. Discharge into Running Reelfoot Bayou was determined using a standard stage-discharge rating curve. Discharge measured using an acoustic Doppler current profiler was used to calibrate gate-discharge equations for free and submerged orifice flow at the new structure.
Two lake operation models, one for the summer season and another for the winter season, are provided for the new structure based on data from this period. The summer operation model is based on operation of the gates once the lake level exceeds an elevation of 282.7 feet (ft) above the North American Vertical Datum of 1988 (NAVD 88). Free flow begins when lake level reaches 282.3 ft above NAVD 88 and becomes transitional once the lake level exceeds 282.8 ft above NAVD 88. Submerged flow begins once the lake level reaches 283 ft above NAVD 88 and the tail-water depth is above critical flow depth. The winter operation model is based on operation of the gates once the lake level exceeds 283.2 ft above NAVD 88. Submerged flow begins when the lake rises to an elevation of 283.5 ft above NAVD 88 and the tail-water depth is above critical flow depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221073","collaboration":"Prepared in cooperation with the Tennessee Wildlife Resources Agency","usgsCitation":"Heal, E.N., Diehl, T.H., and Garrett, J.W., 2022, Preliminary models relating lake level gate operation and discharge at Reelfoot Lake in Tennessee and Kentucky: U.S. Geological Survey Open-File Report 2022–1073, 27 p., https://doi.org/10.3133/ofr20221073.","productDescription":"Report: vii, 27 p.; Data Release; Database","onlineOnly":"Y","ipdsId":"IP-103756","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":406684,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GY1UF4","text":"USGS data release","linkHelpText":"Preliminary model data for lake level gate operation and discharge at Reelfoot Lake—Tennessee and Kentucky"},{"id":501823,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113520.htm","linkFileType":{"id":5,"text":"html"}},{"id":406683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1073/ofr20221073.pdf","text":"Report","size":"2.57 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1073"},{"id":406682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1073/coverthb.jpg"},{"id":406685,"rank":4,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS water data for the Nation—","linkHelpText":"U.S. Geological Survey National Water Information System database"},{"id":406844,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1073/images"},{"id":406845,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1073/ofr20221073.xml"},{"id":409059,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/ofr20221073/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1073"}],"country":"United States","state":"Kentucky, Tennessee","otherGeospatial":"Reelfoot Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.46441650390625,\n 36.30350540784278\n ],\n [\n -89.25155639648438,\n 36.30350540784278\n ],\n [\n -89.25155639648438,\n 36.52453591500483\n ],\n [\n -89.46441650390625,\n 36.52453591500483\n ],\n [\n -89.46441650390625,\n 36.30350540784278\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Dissolved atmogenic gasses in groundwater provide significant information about recharge conditions, flowpath, and age. Free phase gas in aquifers is largely ignored in these analyses and there is a lack of quantitative analysis for gas flux mechanisms. Many related fields encountering multiphase flow acknowledge that the presence of bubbles allows for the rapid exsolution of dissolved gasses and volatile compounds through diffusive and polar forces. By measuring the mass flow of the exsolved gas at a spring, coupled with compositional analysis in the free and dissolved phases, we show that not incorporating the effects of the free gas phase of bubbling springs introduces error in the estimation of total gas quantities, particularly light noble gasses. This can significantly affect the corresponding estimation of noble gas temperature (NGT) and apparent age. We examine the transport of free and dissolved gas from the recharge zone, using water level variation data, to the discharge location where the gases are measured. This technique of using the free gas phase for assessing aquifer dynamics will improve groundwater conceptual models, particularly in karstic aquifers where rapid fluctuations in the water table facilitate the development of excess air, generating multiphase spring discharge.
Tidal wetlands play an important role in global carbon cycling by storing carbon in sediment at millennial time scales, transporting dissolved carbon into coastal waters, and contributing significantly to global CH4 budgets. However, these ecosystems' greenhouse gas monitoring and predictions are challenging due to spatial heterogeneity and tidal flooding. We utilized eddy covariance and chamber measurements to quantify fluxes of CO2 and CH4 at a restored tidal saltmarsh across spatial and temporal scales. Eddy covariance data revealed that the site was a strong net sink for CO2 (−387 g C-CO2 m−2 yr−1, SD = 46) and a small net source of CH4 (0.7 g C-CH4 m−2 yr−1, SD = 0.4). After partitioning net ecosystem exchange of CO2 into gross primary production and ecosystem respiration, we found that high net uptake of CO2 was due to low respiration emissions rather than high photosynthetic rates. We also found that respiration rates varied between land covers with increased respiration in mudflats compared to vegetated areas. Daytime soil chamber measurements revealed that the greatest CO2 emission was from higher elevation mudflat soils (0.5 μmol m−2s−1, SE = 1.3) and CH4 emission was greatest from lower elevation Spartina foliosa soils (1.6 nmol m−2s−1, SD = 8.2). Overall, these results highlight the importance of the relationships between wetland plant community and elevation, and inundation for CO2 and CH4 fluxes. Future research should include the use of high-resolution imagery, automated chambers, and a focus on quantifying carbon exported in tidal waters.
Multiple instruments and methods exist for collecting discrete streamflow measurements in small streams with low flows, defined here as less than 5.7 m3/s (200 ft3/s). Included in the available methods are low-cost approaches that are infrequently used, in part, because their uncertainty is not well known. In this work, we evaluated the accuracy and suitability of three low-cost velocity measurement methods (surface float [SF], velocity head rod [VR], and rising body [RB]) and three conventional current meters (acoustic Doppler velocimeter, and mechanical Price type AA and Price Pygmy meters) relative to discharge calculated from stable artificial hydraulic controls. A total of 231 measurements were made by 20 individuals during 88 site visits to 24 sites in eight states. Accuracies were assessed for all methods and precision was evaluated for the low-cost methods. The median percent error was below 5% for conventional methods, and below 20% for the low-cost methods. The SF was the most accurate (median absolute percent error 14%) and precise (mean percent precision of 11%) low-cost method. The RB and VR, respectively, had 15% and 20% median absolute percent error and 29% and 12% mean percent precision. Results suggest that low-cost methods, when used appropriately, can be used to estimate discharge data under low flow conditions when measurements with conventional methods are not feasible and the associated accuracies meet end-user measurement objectives.
We surveyed for Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) along the upper San Luis Rey River near Lake Henshaw in Santa Ysabel, California, in 2021. Surveys were completed at four locations: three downstream from Lake Henshaw, where surveys occurred from 2015 to 2020 (Rey River Ranch [RRR], Cleveland National Forest [CNF], Vista Irrigation District [VID]), and one at VID Lake Henshaw (VLH) that has been surveyed annually since 2018. There were 78 territorial flycatchers detected at 3 locations (RRR, CNF, VLH), and 1 transient flycatcher of unknown subspecies was detected at VID. Downstream from Lake Henshaw, five flycatchers, including three males and two females, were detected at RRR and CNF. In total, three territories were established, consisting of two pairs and one male of undetermined breeding status. At VLH, we detected 73 flycatchers, including 32 males, 38 females, and 3 flycatchers of unknown sex. In total, 43 territories were established, containing 38 pairs (22 monogamous pairings, 7 confirmed polygynous groups consisting of 7 males each pairing with 2 different females, and 1 suspected polygynous group consisting of 1 male and 2 females), and 5 flycatchers of undetermined breeding status (2 males and 3 flycatchers of unknown sex). Brown-headed cowbirds (Molothrus ater; cowbird) were detected at all four survey locations.
Flycatchers used five habitat types in the survey area: (1) mixed willow riparian, (2) willow-cottonwood, (3) willow-oak, (4) willow-ash, and (5) sycamore-oak. Eighty-seven percent of the flycatchers were detected in habitat characterized as mixed willow riparian, and 94 percent of the flycatchers were detected in habitat with greater than 95-percent native plant cover. Exotic vegetation was not prevalent in the survey area.
There were 15 nests incidentally located during surveys: 1 was successful, 2 were seen with eggs or nestlings on the last visit, 9 failed, and the outcome of the remaining 3 nests was unknown. Three of these nests were parasitized by cowbirds. There were 13 juveniles detected at VLH during surveys; no juveniles were detected at RRR or CNF.
Of the 10 banded flycatchers detected during surveys, 7 were resighted and confirmed to be adults that held territories in previous years. Three flycatchers with a single dark blue federal band, indicating that they were banded as nestlings in the former demographic study area downstream from Lake Henshaw, were resighted during surveys.
In 2021, we documented both adult and natal flycatchers moving from the former demographic study area downstream from Lake Henshaw upstream to the habitat surrounding Lake Henshaw. Three natal flycatchers that were originally banded as nestlings and three adults that previously held territories downstream dispersed to Lake Henshaw in 2021.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1158","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Howell, S.L., and Kus, B.E., 2022, Distribution and abundance of Southwestern Willow Flycatchers (Empidonax traillii extimus) on the Upper San Luis Rey River, San Diego County, California—2021 data summary: Data Report 1158, 11p., https://doi.org/10.3133/dr1158.","productDescription":"Report: viii, 11 p.; Data Release","numberOfPages":"11","onlineOnly":"Y","ipdsId":"IP-136841","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":406717,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1158/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1158"},{"id":405654,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1158/dr1158.xml"},{"id":405655,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1158/images"},{"id":405652,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1158/dr1158.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":405651,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1158/covrthb.jpg"},{"id":405649,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96VC5Y4","text":"Southwestern Willow Flycatcher (Empidonax traillii extimus) surveys and nest monitoring in San Diego County, California","description":"Howell, S.L., and Kus, B.E., 2022, Southwestern Willow Flycatcher (Empidonax traillii extimus) surveys and nest monitoring in San Diego County, California: U.S. Geological Survey data release, available at https://doi.org/10.5066/P96VC5Y4."}],"country":"United States","state":"California","county":"San Diego County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.86363220214844,\n 33.18641002415405\n ],\n [\n -116.69677734375,\n 33.18641002415405\n ],\n [\n -116.69677734375,\n 33.30585555262747\n ],\n [\n -116.86363220214844,\n 33.30585555262747\n ],\n [\n -116.86363220214844,\n 33.18641002415405\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Managers charged with recovering endangered species in regulated river segments often have limited flexibility to alter flow regimes and want estimates of the expected population benefits associated with both flow and nonflow management actions. Disentangling impacts on different life stages from concurrently applied actions is essential for determining the effectiveness of each action, but difficult without models that integrate multiple information sources. Here, we develop and fit an integrated population model for endangered Rio Grande Silvery Minnow (Hybognathus amarus) in the Middle Rio Grande, New Mexico. We integrate catch per unit effort monitoring data collected during 2002–2018 with population estimates, data collected during rescue of minnow from drying pools, habitat availability estimates, laboratory results, releases of hatchery reared minnow, and expert opinion. We use expert elicitation to develop a larval carrying capacity index as an informed proxy for the complex interactions among flow, habitat, and life history in this species. We evaluate the model using out-of-sample forecasts of 2019 and 2020, develop an algorithm to identify supplemental water releases that maximize benefits to the minnow, and quantify the effectiveness of various actions. Experts generally agreed on the duration and timing of flow requirements and disagreed regarding the importance of different magnitudes. The integrated model with the larval carrying capacity index outperformed two alternative models in forecasting catch in 2019 and 2020. The model estimates that minnow abundance varied by more than three orders of magnitude between 2002 and 2018 and that in a few years recruitment was limited by spawner abundance. Evaluation of the expected benefits of flow and nonflow management actions to fall population abundance across different years suggests that efficient addition of water to the base hydrograph is the most effective action in most, but not all years. Many actions are effective only under certain hydrologic and population conditions and the effectiveness of different actions varies in different sections of the study area. Widespread water extraction and river regulation combined with periodic drought and ongoing climate change may necessitate creative management of federally listed fish species in arid systems informed by thorough analyses of management effectiveness.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4240","usgsCitation":"Yackulic, C., Archdeacon, T.P., Valdez, R.A., Hobbs, M., Porter, M., Lusk, J., Tanner, A.M., Gonzales, E., Lee, D.Y., and Haggerty, G.M., 2022, Quantifying flow and nonflow management impacts on an endangered fish by integrating data, research, and expert opinion: Ecosphere, v. 13, no. 9, e4240, 22 p., https://doi.org/10.1002/ecs2.4240.","productDescription":"e4240, 22 p.","ipdsId":"IP-138221","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":446432,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4240","text":"Publisher Index Page"},{"id":407407,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-09-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":853029,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Archdeacon, Thomas P","contributorId":296980,"corporation":false,"usgs":false,"family":"Archdeacon","given":"Thomas","email":"","middleInitial":"P","affiliations":[{"id":64264,"text":"U.S. Fish & Wildlife Service, New Mexico Fish & Wildlife Conservation Office, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853030,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Valdez, Richard A.","contributorId":204243,"corporation":false,"usgs":false,"family":"Valdez","given":"Richard","email":"","middleInitial":"A.","affiliations":[{"id":34515,"text":"SWCA Environmental Consultants","active":true,"usgs":false}],"preferred":false,"id":853031,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hobbs, Monika","contributorId":296981,"corporation":false,"usgs":false,"family":"Hobbs","given":"Monika","email":"","affiliations":[{"id":64265,"text":"Albuquerque Bernalillo County Water Utility Authority, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853032,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Porter, Michael D.","contributorId":139912,"corporation":false,"usgs":false,"family":"Porter","given":"Michael D.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":853033,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lusk, Joel","contributorId":296982,"corporation":false,"usgs":false,"family":"Lusk","given":"Joel","email":"","affiliations":[{"id":64266,"text":"US Bureau of Reclamation, Environment and Lands Division, Albuquerque, NM","active":true,"usgs":false}],"preferred":false,"id":853034,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tanner, Ashley M.","contributorId":264589,"corporation":false,"usgs":false,"family":"Tanner","given":"Ashley","email":"","middleInitial":"M.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":853035,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gonzales, Eric J","contributorId":296983,"corporation":false,"usgs":false,"family":"Gonzales","given":"Eric J","affiliations":[{"id":64267,"text":"U.S. Bureau of Reclamation, Albuquerque Area Office, Environment & Lands Division, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853036,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lee, Debbie Y","contributorId":296984,"corporation":false,"usgs":false,"family":"Lee","given":"Debbie","email":"","middleInitial":"Y","affiliations":[{"id":64268,"text":"Western EcoSystems Technology, Inc., Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853037,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Haggerty, Grace M","contributorId":296985,"corporation":false,"usgs":false,"family":"Haggerty","given":"Grace","email":"","middleInitial":"M","affiliations":[{"id":64269,"text":"New Mexico Interstate Stream Commission, Albuquerque, NM, USA","active":true,"usgs":false}],"preferred":false,"id":853038,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70236690,"text":"70236690 - 2022 - Development of the LCMAP annual land cover product across Hawai'i","interactions":[],"lastModifiedDate":"2023-11-08T16:45:41.692299","indexId":"70236690","displayToPublicDate":"2022-09-14T09:22:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2027,"text":"International Journal of Applied Earth Observation and Geoinformation","active":true,"publicationSubtype":{"id":10}},"title":"Development of the LCMAP annual land cover product across Hawai'i","docAbstract":"Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the U.S. Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full Landsat records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the LCMAP framework to process annual land cover maps in Hawaiʻi is to collect sufficient high-quality training data. Although multiple datasets depicting land cover information are available in Hawaiʻi, they covered limited time frames and were produced from various remote sensing sources with different, classification categories, spatial resolution, and mapping accuracies. No solo product is suitable to provide LCMAP training data labels on its own. In this paper, we focused on enhancing the LCMAP training datasets to generate land cover products from 2000 to 2019 in Hawaiʻi. A total of 200 independent reference data plots were generated and manually interpreted for validating the mapping results produced by the training datasets. The results revealed that using the appropriate filter of multiple products as training data pools improved the classification model performance. The effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types were summarized. The LCMAP land surface change products for Hawaiʻi are available at https://doi.org/10.5066/P91E8M23.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jag.2022.103015","usgsCitation":"Li, C., Xian, G.Z., Wellington, D., Smith, K., Horton, J., and Zhou, Q., 2022, Development of the LCMAP annual land cover product across Hawai'i: International Journal of Applied Earth Observation and Geoinformation, v. 113, 103015, 17 p., https://doi.org/10.1016/j.jag.2022.103015.","productDescription":"103015, 17 p.","ipdsId":"IP-144117","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":446437,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jag.2022.103015","text":"Publisher Index Page"},{"id":406839,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"113","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":851901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":851902,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wellington, Danika F. 0000-0002-2130-0075","orcid":"https://orcid.org/0000-0002-2130-0075","contributorId":237074,"corporation":false,"usgs":false,"family":"Wellington","given":"Danika F.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":851903,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Kelcy 0000-0001-6811-1485","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":272037,"corporation":false,"usgs":false,"family":"Smith","given":"Kelcy","affiliations":[{"id":56338,"text":"KBR, Inc., Contractor under USGS","active":true,"usgs":false}],"preferred":false,"id":851904,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horton, Josephine 0000-0001-8436-4095","orcid":"https://orcid.org/0000-0001-8436-4095","contributorId":191430,"corporation":false,"usgs":false,"family":"Horton","given":"Josephine","affiliations":[],"preferred":false,"id":851905,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":851906,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70256654,"text":"70256654 - 2022 - Climate change alters aging patterns of reservoir aquatic habitats","interactions":[],"lastModifiedDate":"2024-08-29T15:12:56.295325","indexId":"70256654","displayToPublicDate":"2022-09-13T10:03:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Climate change alters aging patterns of reservoir aquatic habitats","docAbstract":"Two slow-moving developments are threatening reservoir aquatic habitats globally: aging and climate change. These events are projected to transform reservoir aquatic habitats in various and often unpredictable ways. Aging affects in-lake habitats directly, whereas climate change affects both in-lake and off-lake conditions. Climate change is expected to accelerate and, in some instances, possibly decelerate aging. Aging can be indexed as functional age, an index that signals the position of a reservoir along its lifespan relying on in-lake descriptors of aquatic habitat. Using existing habitat datasets and climate projections, we developed semi-quantitative predictions about the effect of climate change on reservoir functional age in the USA. Driven by increased warming, functional age was predicted to increase latitudinally from south to north with no obvious longitudinal gradient. Functional age also changed with precipitation, increasing latitudinally from south to north and longitudinally in the east and west but decreasing in the central USA. Our projections are tentative because of the uncertain nature of reservoir aging and climate change sciences, as well as the inexactness of available data and models. We review general strategies suitable for systematically dealing with the unpredictable and constantly changing conditions expected to occur this century as reservoirs certainly continue to get older, within the scope of uncertain climate change projections.
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Unit","active":true,"usgs":false}],"preferred":false,"id":908512,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70235865,"text":"sir20175070C - 2022 - Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota—Water resources","interactions":[{"subject":{"id":70235865,"text":"sir20175070C - 2022 - Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota—Water resources","indexId":"sir20175070C","publicationYear":"2022","noYear":false,"chapter":"C","displayTitle":"Potential Effects of Energy Development on Environmental Resources of the Williston Basin in Montana, North Dakota, and South Dakota—Water Resources","title":"Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota—Water resources"},"predicate":"IS_PART_OF","object":{"id":70191166,"text":"sir20175070 - 2022 - Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota","indexId":"sir20175070","publicationYear":"2022","noYear":false,"title":"Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota"},"id":1}],"isPartOf":{"id":70191166,"text":"sir20175070 - 2022 - Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota","indexId":"sir20175070","publicationYear":"2022","noYear":false,"title":"Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota"},"lastModifiedDate":"2026-04-01T15:49:03.755037","indexId":"sir20175070C","displayToPublicDate":"2022-09-13T06:05:15","publicationYear":"2022","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":"2017-5070","chapter":"C","displayTitle":"Potential Effects of Energy Development on Environmental Resources of the Williston Basin in Montana, North Dakota, and South Dakota—Water Resources","title":"Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota—Water resources","docAbstract":"The Williston Basin has been a leading oil and gas producing area for more than 50 years. While oil production initially peaked within the Williston Basin in the mid-1980s, production rapidly increased in the mid-2000s, largely because of improved horizontal (directional) drilling and hydraulic fracturing methods. In 2012, energy development associated with the Bakken Formation was identified as a priority requiring collaboration toward improved timeliness of issuing permits for new wells combined with reasonable measures to maintain environmental quality. Shortly thereafter, the Bakken Federal Executive Group was created to address common challenges associated with energy development. The Bakken Federal Executive Group partner agencies identified a gap in current understanding of the cumulative environmental challenges attributed to energy development throughout the area, resulting in an effort to aggregate scientific data and identify additional research and information needs related to natural resources within areas of energy development in the Williston Basin. As part of this effort, water resources in the area (including groundwater; streams and rivers; and lakes, reservoirs, and wetlands) were characterized and described in terms of physical occurrence, flow characteristics, recharge, water quality, and water use. Similarly, waters produced during energy-development activities also were characterized even though these waters are not considered usable resources within the area. Groundwater resources were characterized by the major hydrogeologic units, or aquifers, identifying the units that supply most groundwater used for domestic, stock, agricultural, and industrial purposes. The groundwater characterization included other deeper hydrogeologic units in the Williston Basin that may be a useable source of water with treatment, have utility as a reservoir for reinjection of produced waters, or be a source of minerals and energy resources. A generalized groundwater budget and flow system identifying the sources of recharge (stream infiltration, precipitation, and movement [leakage] from other aquifers) and the general groundwater flow direction is included for each of the major hydrogeologic units. Rivers and streams within the Williston Basin with 10 or more years of continuous streamflow data were identified. For a subset of these sites, streamflow characteristics, including the monthly and annual mean flow, were generated to identify seasonal and interannual changes in streamflow and thus provide information on the drivers and reliability of streamflow at the seasonal or multiyear scale. Daily streamflow and annual extreme flows (peak and low flow) also were estimated for the subset of sites. The daily streamflow and annual extreme flow values provide information on short-term or extreme events that are relevant to infrastructure design and evaluating spills, leaks, or accidental discharges of water or petroleum products. Surface-water features (lakes, ponds, and wetlands) were classified using the Cowardin system and identified on the National Wetlands Inventory maps generated by the U.S. Fish and Wildlife Service. The spatial distribution of the surface-water features was analyzed by State, county, and specifically in comparison to the Prairie Pothole Region. The proximity of the surface-water features to energy development infrastructure (specifically oil or gas well pads) was evaluated. It was determined that, although oil or gas wells are often near a surface-water feature, most surface-water features do not have wells nearby, with the exception of wells in the Prairie Pothole Region. Water-quality data were aggregated from two data sources: (1) the Water-Quality Portal, sponsored by the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), and National Water Quality Monitoring Council; and (2) a data compilation completed as part of the USGS National Water-Quality Assessment project. The Water-Quality Portal integrates publicly available water-quality data from databases maintained by the USGS, EPA, and U.S. Department of Agriculture, including water-quality data from Tribal, State, and local databases. Water-quality data for 15 commonly measured water-quality constituents were aggregated for groundwater, rivers and streams, and lakes and reservoirs. For each aggregated dataset (groundwater, rivers and streams, and lakes and reservoirs), analyses of the water-quality data included summary statistics, maps of spatial distribution of constituent values, boxplots of constituent values by timeframe or hydrogeologic unit, spatial comparisons of site locations and constituent values to petroleum well density, and comparisons of the constituent values measured to EPA drinking-water standards/guidelines. Produced water includes all fluids brought to the surface along with the targeted hydrocarbons as part of the oil and gas exploration and extraction processes. These fluids may include formation water (waters that co-exist with rock/oil/gas), hydraulic fracturing fluids, and other combinations of water and chemicals used during oil and gas well drilling, development, treatments, recompletions, and workovers. Produced water datasets were aggregated from two sources: the USGS National Produced Waters Geochemical database (ver. 2.1) and a series of projects focused specifically on sampling produced water in the Williston Basin from 2010 to 2014. The National Produced Waters Geochemical database was useful for a general understanding of produced-water chemistry. Produced waters are characterized by extreme salinity and contain elevated concentrations of other constituents (including arsenic, barium, cadmium, lead, zinc, radium-226/radium-228, and ammonium) that could negatively affect water and aquatic resources if released. Produced waters also have a generally unique chemical (isotopic) signature that may be useful in tracking water from different geologic units; for example, the oxygen/deuterium and strontium ratio values measured in brine waters from the Bakken Formation are distinct from brines collected from other geologic units in the Williston Basin.
Water-use information related to energy production in the area also was aggregated and summarized. The summary of water use is not limited to oil and gas production but includes water used to produce all types of energy resources in the Williston Basin, including coal/lignite, thermoelectric power, oil and gas, hydropower, biomass and biofuels, wind, geothermal, and solar. Each State has its own methods for regulating and reporting water usage within its jurisdiction. These methods can introduce problems when examining water use from sources, such as the Missouri River or Fox Hills aquifer, that are shared across political boundaries. Without the one-to-one match for usage types and amounts used from a water source, it is difficult to develop a comprehensive water budget for the water source being evaluated. A large amount of freshwater is required to prepare a well for oil and gas well production; in some cases, 3 to 7 million gallons of water are needed per well. The EPA estimates that hydraulic fracturing in the Williston Basin uses between 70 to 140 billion gallons per year. Water also is used for myriad other purposes related to ancillary oil and gas extraction. In addition to water used for immediate energy development, the expanded human workforce migrating into the area and other support staff who have moved into the area during the development also use water.
Research and information needs were identified that could be relevant in the evaluation of the effects of energy development on water resources. Information needs related to the evaluation of groundwater resources include the following: improved potentiometric-surface maps for glacial units; availability of a uniform stream network digital geographic coverage that spans the international boundary with Canada; enhanced surface-water use information with regards to the gain and loss of streamflow to shallow groundwater, which would increase understanding groundwater and surface-water interactions; and expanded geophysical assessments. Gaps in the availability of streamflow data include the lack of information on ice-jam flooding despite potential for effects to infrastructure (pipelines, roads, and facilities) and an understanding of the cumulative effects of largely undocumented stock and diversion dams. Although this study resulted in the aggregation of a large quantity of water-quality data, the availability of consistently collected, systematically processed and reported data over large parts of the Williston Basin is sparse. Few samples have been analyzed for constituents that may indicate the effect of energy development on water resources. Constituents that could be considered include boron, chloride, bromide, iodine, fluoride, manganese, lithium, radium, strontium isotopes, volatile organic compounds, and isotopes of inorganic ions (such as hydrogen and carbon). Collaboration between Tribal, Federal, State, and local entities to identify a common study design, common monitoring constituents, and consistent sampling locations would generate datasets with broad utility and would likely result in overall cost savings for monitoring over time. Similarly, there is a need for standardized sample collection, processing, laboratory analytical methods, and the collection of ancillary data for produced waters sampling. Additional characterization of the range of chemical, microbial, and isotopic compositions and quantities of “end-member” produced waters, and the collection of time-series datasets to document the changes in produced waters during and after well development also were needs identified during this study. Water-use estimates would be improved through the implementation of comprehensive studies of water use from groundwater and surface-water sources using consistent methodologies across the Williston Basin. The submission of chemical and water data related to hydraulic fracturing collected by the oil and gas industry would add to the quantity of available data. Consistent implementation of regulations and monitoring controls across political boundaries (State, county, and international) would further improve the consistency of data available for the estimates of water use.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Potential Effects of Energy Development on Environmental Resources of the Williston Basin in Montana, North Dakota, and South Dakota","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175070C","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Bartos, T.T., Sando, S.K., Preston, T.M., Delzer, G.C., Lundgren, R.F., Nustad, R.A., Caldwell, R.R., Peterman, Z.E., Smith, B.D., Macek-Rowland, K.M., Bender, D.A., Frankforter, J.D., and Galloway, J.M., 2022, Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota—Water resources (ver. 1.1, October 2022): U.S. Geological Survey Scientific Investigations Report 2017–5070–C, 159 p., https://doi.org/10.3133/sir20175070C.","productDescription":"Report: xv, 159 p.; 1 Figure: 8.50 × 11.00 inches; Data Release","numberOfPages":"180","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-082945","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":405463,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5070/c/coverthb2.jpg"},{"id":408159,"rank":4,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5070/c/sir20175070c.pdf","text":"Report","size":"25.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5070–C"},{"id":408160,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5070/c/versionHist.txt","text":"Version History","size":"2.88 kB","linkFileType":{"id":2,"text":"txt"}},{"id":405466,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F77080B9","text":"USGS data release","linkHelpText":"Results of water resource data aggregations within areas of energy development in the Williston Basin in Montana, North Dakota, and South Dakota"},{"id":501945,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113506.htm","linkFileType":{"id":5,"text":"html"}},{"id":405465,"rank":2,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5070/c/sir20175070c_fig09.pdf","text":"Figure 9 (layered)","size":"5.97 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.5,\n 45.120052841530544\n ],\n [\n -96.94335937499999,\n 45.120052841530544\n ],\n [\n -96.94335937499999,\n 49.009050809382046\n ],\n [\n -108.5,\n 49.009050809382046\n ],\n [\n -108.5,\n 45.120052841530544\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: September 13, 2022; Version 1.1: October 18, 2022","contact":"Director, Wyoming-Montana Water Science Center
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Disturbances elicit both positive and negative effects on organisms; these effects vary in their strength and their timing. Effects of disturbance interval (i.e., the length of time between disturbances) on population growth will depend on both the timing and strength of positive and negative effects of disturbances. Climate change can modify the relative strengths of these positive and negative effects, leading to altered optimal disturbance intervals (the disturbance interval at which population growth rate is highest) and changes in the sensitivity of population growth rate to disturbance interval. While we know that climate may alter impacts of disturbance in some systems, we have a poor understanding of which effects of disturbance and which vital rates might drive an altered response to disturbance interval in a changing climate. We use demographic monitoring of natural populations of Dionaea muscipula, the Venus flytrap, that have experienced natural and managed fires, combined with realistic past and future climate projections, to construct climate- and fire-driven integral projection models (IPMs). We use these IPMs to compare the effect of fire return interval (FRI) on population growth rate in past and future climates. To dissect the mechanisms driving FRI response, we then construct IPMs with demographic data from an experimental manipulation of fire effects (ash addition, neighbor removal) and an accidental fire. Our results show that an FRI of 10 years is optimal for D. muscipula in past climate conditions, but a longer FRI (12 years) is optimal in future climate conditions. Further, deviations from optimal FRI reduce population growth rate dramatically in the past climate, but this reduction is muted in a future climate (future minus past sensitivity = 0.006, 95% CI [0.002, 0.011]). Finally, our experimental work suggests that fire effects are driven in part by positive, additive effects of competitor removal and ash addition immediately following a fire; for one population, both these treatments significantly increased population growth rate. Our work suggests that climate change can alter the response of populations to disturbance, highlighting the need to consider the interacting effects of multiple abiotic drivers when projecting future population growth and geographical distributions.
","language":"English","publisher":"Wiley","doi":"10.1002/ecm.1528","usgsCitation":"Louthan, A.M., Keighron, M., Kiekebusch, E., Cayton, H., Terando, A., and Morris, W., 2022, Climate change weakens the impact of disturbance interval on the growth rate of natural populations of Venus flytrap: Ecological Monographs, v. 92, e1528, 18 p., https://doi.org/10.1002/ecm.1528.","productDescription":"e1528, 18 p.","ipdsId":"IP-114086","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":446451,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecm.1528","text":"Publisher Index Page"},{"id":406517,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.310791015625,\n 34.59478059328729\n ],\n [\n -76.7230224609375,\n 34.59478059328729\n ],\n [\n -76.7230224609375,\n 35.018750379438295\n ],\n [\n -77.310791015625,\n 35.018750379438295\n ],\n [\n -77.310791015625,\n 34.59478059328729\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.28970336914062,\n 35.04011643687423\n ],\n [\n -78.8818359375,\n 35.04011643687423\n ],\n [\n -78.8818359375,\n 35.3308118573182\n ],\n [\n -79.28970336914062,\n 35.3308118573182\n ],\n [\n -79.28970336914062,\n 35.04011643687423\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"92","noUsgsAuthors":false,"publicationDate":"2022-07-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Louthan, Allison M","contributorId":266009,"corporation":false,"usgs":false,"family":"Louthan","given":"Allison","email":"","middleInitial":"M","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":851461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Keighron, Melina","contributorId":296421,"corporation":false,"usgs":false,"family":"Keighron","given":"Melina","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":851462,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiekebusch, Elsita","contributorId":257676,"corporation":false,"usgs":false,"family":"Kiekebusch","given":"Elsita","email":"","affiliations":[{"id":13595,"text":"NCSU","active":true,"usgs":false}],"preferred":false,"id":851463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cayton, Heather","contributorId":229344,"corporation":false,"usgs":false,"family":"Cayton","given":"Heather","email":"","affiliations":[{"id":41625,"text":"Kellogg Biological Station and Department of Integrative Biology, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":851464,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Terando, Adam J. 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":216875,"corporation":false,"usgs":true,"family":"Terando","given":"Adam J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":851465,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Morris, William F.","contributorId":266011,"corporation":false,"usgs":false,"family":"Morris","given":"William F.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":851466,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70236586,"text":"70236586 - 2022 - A machine learning approach to predicting equilibrium ripple wavelength","interactions":[],"lastModifiedDate":"2022-09-28T16:48:59.256996","indexId":"70236586","displayToPublicDate":"2022-09-12T08:11:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7599,"text":"Environmental Modeling and Software","active":true,"publicationSubtype":{"id":10}},"title":"A machine learning approach to predicting equilibrium ripple wavelength","docAbstract":"Sand ripples are geomorphic features on the seafloor that affect bottom boundary layer dynamics including wave attenuation and sediment transport. We present a new equilibrium ripple predictor using a machine learning approach that outputs a probability distribution of wave-generated equilibrium wavelengths and statistics including an estimate of ripple height, the most probable ripple wavelength, and sediment and flow parameterizations. The Bayesian Optimal Model System (BOMS) is an ensemble machine learning system that combines two machine learning algorithms and two deterministic empirical ripple predictors with a Bayesian meta-learner to produce probabilistic wave-generated equilibrium ripple wavelength estimates in sandy locations. A ten-fold cross validation of BOMS resulted in an adjusted R-squared value of 0.93 and an average root mean square error (RMSE) of 8.0 cm. During both cross validation and testing on three unique field datasets, BOMS provided more accurate wavelength predictions than each individual base model and other common ripple predictors.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2022.105509","usgsCitation":"Phillip, R.E., Penko, A.M., Palmsten, M.L., and DuVal, C.B., 2022, A machine learning approach to predicting equilibrium ripple wavelength: Environmental Modeling and Software, v. 157, 105509, 13 p., https://doi.org/10.1016/j.envsoft.2022.105509.","productDescription":"105509, 13 p.","ipdsId":"IP-133890","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446454,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2022.105509","text":"Publisher Index Page"},{"id":406515,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"157","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Phillip, Ryan E.","contributorId":296413,"corporation":false,"usgs":false,"family":"Phillip","given":"Ryan","email":"","middleInitial":"E.","affiliations":[{"id":62875,"text":"U.S. Naval Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":851445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Penko, Allison M.","contributorId":296414,"corporation":false,"usgs":false,"family":"Penko","given":"Allison","email":"","middleInitial":"M.","affiliations":[{"id":62875,"text":"U.S. Naval Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":851446,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":851447,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DuVal, Carter B.","contributorId":296415,"corporation":false,"usgs":false,"family":"DuVal","given":"Carter","email":"","middleInitial":"B.","affiliations":[{"id":62875,"text":"U.S. Naval Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":851448,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70241526,"text":"70241526 - 2022 - Forecasting explosions at Sinabung Volcano, Indonesia, based on SO2 emission rates","interactions":[],"lastModifiedDate":"2023-03-22T11:53:19.487909","indexId":"70241526","displayToPublicDate":"2022-09-12T06:51:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting explosions at Sinabung Volcano, Indonesia, based on SO2 emission rates","docAbstract":"Dome-building volcanic eruptions are often associated with frequent Vulcanian explosions, which constitute a substantial threat to proximal communities. One proposed mechanism driving such explosions is the sealing of the shallow volcanic system followed by pressurization due to gas accumulation beneath the seal. We investigate this hypothesis at Sinabung Volcano (Sumatra, Indonesia), which has been in a state of eruption since August 2010. In 2013, the volcano began erupting a lava dome and lava flow, and frequent explosions produced eruptive columns that rose many kilometers into the atmosphere and at times sent pyroclastic density currents down the southeast flanks. A network of scanning Differential Optical Absorption Spectrometers (DOAS) was installed on the volcano’s eastern flank in 2016 to continuously monitor SO2 emission rates during daytime hours. Analysis of the DOAS data from October 2016 to September 2017 revealed that passive SO2 emissions were generally lower in the 5 days leading up to explosive events (∼100 t/d) than was common in 5-day periods leading up to days on which no explosions occurred (∼200 t/d). The variability of passive SO2 emissions, expressed as the standard deviation, also took on a slightly wider range of values before days with explosions (0–103 t/d at 1-sigma) than before days without explosions (43–117 t/d). These observations are consistent with the aforementioned seal-failure model, where the sealing of the volcanic conduit blocks gas emissions and leads to pressurization and potential Vulcanian explosions. We develop a forecasting methodology that allows calculation of a relative daily explosion probability based solely on measurements of the SO2 emission rate in the preceding days. We then calculate forecast explosion probabilities for the remaining SO2 emissions dataset (October 2017—September 2021). While the absolute accuracy of forecast explosion probabilities is variable, the method can inform the probability of an explosion occurring relative to that on other days in each test period. This information can be used operationally by volcano observatories to assess relative risk. The SO2 emissions-based forecasting method is likely applicable to other open vent volcanoes experiencing dome-forming eruptions.