Accurate estimates of flood magnitude and frequency are needed to (1) optimize the design and location of infrastructure, including dams, culverts, bridges, industrial buildings, and highways, and (2) inform flood-zoning and flood-insurance studies. The U.S. Geological Survey (USGS), in cooperation with the State of Hawaiʻi Department of Transportation, estimated flood magnitudes for the 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (AEP) for unregulated streamgages in Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, using data through water year 2020. Regression equations were developed to estimate flood magnitude and associated frequency at ungaged streams. This study improves upon a previous USGS flood-frequency report (Oki and others, 2010) by including more peak-flow data, implementing new statistical methods in flood-frequency analysis, and using updated techniques to estimate the regional-skewness coefficient (regional skew).
Flood magnitude and frequency at 238 streamgages were estimated—following national guidelines established in Bulletin 17C (England and others, 2019)—by fitting annual peak-flow data to the Log-Pearson Type III distribution using the expected moments algorithm and the PeakFQ flood-frequency software. Potentially influential low outliers in the data were identified and removed using the Multiple Grubbs-Beck Test. An updated regional skew for Hawaiʻi was estimated using the Bayesian weighted least squares/Bayesian generalized least squares method. The updated regional skew employs a constant model for the five islands in the study area and has a value of −0.157 (mean square error of 0.212).
Multiple linear regression techniques were used to develop regression equations that relate basin and climatic characteristics to peak flows at streamgages. The regression equations can be applied to estimate flood magnitude and frequency at ungaged sites. The study area was split into 10 regions—2 regions per island, generally following a leeward/windward division—containing from 9 to 49 streamgages each. The final regression equations for each region were determined with generalized least-squares analysis using the USGS weighted-multiple-linear regression (WREG) program. The standard error of prediction at the 1-percent AEP for the regression equations ranged from 18 to 164 percent; the pseudo coefficient of determination (pseudo-R2) at the 1-percent AEP ranged from 46 to 100 percent. The regression equations performed well for all regions except leeward Molokaʻi and southern Island of Hawaiʻi; for all other regions, the pseudo-R2 values ranged from about 75 to 100 percent. Compared to the regression equations developed by Oki and others (2010), the regression equations in this study generally showed modest improvements, although the magnitude of differences varied for each region.
Peak-flow estimates at the 238 streamgages included in this study are improved by weighting the at-site statistics computed with PeakFQ and the predicted flows based on the regression equations. Results of this study—including the final peak-flow estimates at streamgages and the regional regression equations—are implemented in the USGS StreamStats web application (U.S. Geological Survey, 2023, StreamStats: https://streamstats.usgs.gov/ss/). StreamStats provides a consistent approach for obtaining peak-flow estimates at streamgages and for applying the regional regression equations for estimating peak flows at ungaged locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235014","collaboration":"Prepared in cooperation with the State of Hawaiʻi Department of Transportation","usgsCitation":"Mitchell, J.N., Wagner, D.M., and Veilleux, A.G., 2023, Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020: U.S. Geological Survey Scientific Investigations Report 2023–5014, 66 p. plus 4 appendixes, https://doi.org/10.3133/sir20235014.","productDescription":"Report: vii, ; 8 Tables; 3 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-139812","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":416577,"rank":15,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GGPPV5","text":"USGS data release","description":"USGS data release","linkHelpText":"Data in support of flood-frequency report—Magnitude and frequency of floods on Kauaʻi, Oʻahu, Molokaʻi, Maui, and Hawaiʻi, State of Hawaiʻi, based on data through water year 2020"},{"id":416576,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TOQANM","text":"USGS data release","description":"USGS data release","linkHelpText":"Basin characteristic rasters used in the update of Hawaiʻi StreamStats, 2022"},{"id":416575,"rank":13,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N61WJ7","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for watershed delineation used in the update of Hawaiʻi StreamStats, 2022"},{"id":416566,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5014/coverthb.jpg"},{"id":416567,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5014/sir20235014.pdf","text":"Report","size":"7.4 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States","state":"Hawaii","otherGeospatial":"Kauaʻi, Oʻahu, Molokaʻi, Maui, Hawaiʻi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -159.92521972722102,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 18.78261358926393\n ],\n [\n -154.69797609100146,\n 22.39025206306377\n ],\n [\n -159.92521972722102,\n 22.39025206306377\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Pacific Islands Science Center
U.S. Geological Survey
1845 Wasp Blvd., B176
Honolulu, HI 96818
Geodetic velocity data provide first-order constraints on crustal surface strain rates, which in turn are linked to seismic hazard. Estimating the 2-D surface strain tensor everywhere requires knowledge of the surface velocity field everywhere, while geodetic data such as Global Navigation Satellite System (GNSS) only have spatially scattered measurements on the surface of the Earth. To use these data to estimate strain rates, some type of interpolation is required. In this study, we review methodologies for strain rate estimation and compare a suite of methods, including a new implementation based on the geostatistical method of kriging, to compare variation between methods with uncertainty based on one method. We estimate the velocity field and calculate strain rates in southern California using a GNSS velocity field and five different interpolation methods to understand the sources of variability in inferred strain rates. Uncertainty related to data noise and station spacing (aleatoric uncertainty) is minimal where station spacing is dense and maximum far from observations. Differences between methods, related to epistemic uncertainty, are usually highest in areas of high strain rate due to differences in how gradients in the velocity field are handled by different interpolation methods. Parameter choices, unsurprisingly, have a strong influence on strain rate field, and we propose the traditional L-curve approach as one method for quantifying the inherent trade-off between fit to the data and models that are reflective of tectonic strain rates. Doing so, we find total variability between five representative strain rate models to be roughly 40 per cent, a much lower value than roughly 100 per cent that was found in previous studies (Hearn et al.). Using multiple methods to tune parameters and calculate strain rates provides a better understanding of the range of acceptable models for a given velocity field. Finally, we present an open-source Python package (Materna et al.) for calculating strain rates, Strain_2D, which allows for the same data and model grid to be used in multiple strain rate methods, can be extended with other methods from the community, and provides an interface for comparing strain rate models, calculating statistics and estimating strain rate uncertainty for a given GNSS data set.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggad191","usgsCitation":"Maurer, J., and Materna, K.Z., 2023, Quantification of geodetic strain rate uncertainties and implications for seismic hazard estimates: Geophysical Journal International, v. 234, no. 3, p. 2128-2142, https://doi.org/10.1093/gji/ggad191.","productDescription":"15 p.","startPage":"2128","endPage":"2142","ipdsId":"IP-142818","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":443642,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggad191","text":"Publisher Index Page"},{"id":435346,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JJW0DY","text":"USGS data release","linkHelpText":"Strain_2D: a package to compute and compare strain rate maps from geodetic velocities"},{"id":417484,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"234","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Maurer, Jeremy","contributorId":305786,"corporation":false,"usgs":false,"family":"Maurer","given":"Jeremy","email":"","affiliations":[{"id":37501,"text":"Missouri University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":873851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Materna, Kathryn Zerbe 0000-0002-6687-980X","orcid":"https://orcid.org/0000-0002-6687-980X","contributorId":261337,"corporation":false,"usgs":true,"family":"Materna","given":"Kathryn","email":"","middleInitial":"Zerbe","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":873852,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243951,"text":"70243951 - 2023 - Estimated reduction of nitrogen in streams of the Chesapeake Bay in areas with agricultural conservation practices","interactions":[],"lastModifiedDate":"2023-05-26T12:02:55.072234","indexId":"70243951","displayToPublicDate":"2023-05-05T07:01:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11111,"text":"PLOS Water","active":true,"publicationSubtype":{"id":10}},"title":"Estimated reduction of nitrogen in streams of the Chesapeake Bay in areas with agricultural conservation practices","docAbstract":"Spatial data provided by the U.S. Department of Agriculture National Resource Conservation Service representing implementation at the field-level for a selection of agricultural conservation practices were incorporated within a spatially referenced regression model to estimate their effects on nitrogen loads in streams in the Chesapeake Bay watershed. Conservation practices classified as “high-impact” were estimated to be effective (p = 0.017) at reducing contemporary nitrogen loads to streams of the Chesapeake Bay watershed in areas where groundwater ages are estimated to be less than 14-years old. Watershed-wide, high-impact practices were estimated to reduce nitrogen loads to streams by 1.45%, with up to 60% reductions in areas with shorter groundwater ages and larger amounts of implementation. Effects of “other-impact” practices and practices in areas with groundwater ages of 14 years or more showed less evidence of effectiveness. That the discernable impact of high-impact practices was limited to areas with a median groundwater age of less than 14 years does not imply that conservation practices are not effective in areas with older groundwater ages. A model recalibrated using high-impact agricultural conservation practice data summarized by county suggests effects may also be detectable using implementation data available at such coarser resolution. Despite increasing investment, effects of agricultural conservation practices on regional water quality remain difficult to quantify due to factors such as groundwater travel times, varying modes-of-action, and the general lack of high-quality spatial datasets representing practice implementation.
Convergent adaptation to the same environment by multiple lineages frequently involves rapid evolutionary change at the same genes, implicating these genes as important for environmental adaptation. Such adaptive molecular changes may yield either change or loss of protein function; loss of function can eliminate newly deleterious proteins or reduce energy necessary for protein production. We previously found a striking case of recurrent pseudogenization of the Paraoxonase 1 (Pon1) gene among aquatic mammal lineages—Pon1 became a pseudogene with genetic lesions, such as stop codons and frameshifts, at least four times independently in aquatic and semiaquatic mammals. Here, we assess the landscape and pace of pseudogenization by studying Pon1 sequences, expression levels, and enzymatic activity across four aquatic and semiaquatic mammal lineages: pinnipeds, cetaceans, otters, and beavers. We observe in beavers and pinnipeds an unexpected reduction in expression of Pon3, a paralog with similar expression patterns but different substrate preferences. Ultimately, in all lineages with aquatic/semiaquatic members, we find that preceding any coding-level pseudogenization events in Pon1, there is a drastic decrease in expression, followed by relaxed selection, thus allowing accumulation of disrupting mutations. The recurrent loss of Pon1 function in aquatic/semiaquatic lineages is consistent with a benefit to Pon1 functional loss in aquatic environments. Accordingly, we examine diving and dietary traits across pinniped species as potential driving forces of Pon1 functional loss. We find that loss is best associated with diving activity and likely results from changes in selective pressures associated with hypoxia and hypoxia-induced inflammation.
","language":"English","publisher":"Society for Molecular Biology and Evolution","doi":"10.1093/molbev/msad104","usgsCitation":"Graham, A.M., Jamison, J.M., Bustos, M., Cournoyer, C., Michaels, A., Presnell, J.S., Richter, R., Crocker, D., Fustukjian, A., Hunter, M., Rea, L.D., Marsillach, J., Furlong, C.E., Meyer, W.K., and Clark, N.L., 2023, Reduction of paraoxonase expression followed by inactivation across independent semiaquatic mammals suggests stepwise path to pseudogenization: Molecular Biology and Evolution, v. 40, no. 5, msad104 , 17 p., https://doi.org/10.1093/molbev/msad104.","productDescription":"msad104 , 17 p.","ipdsId":"IP-149663","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":443648,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/molbev/msad104","text":"Publisher Index Page"},{"id":418453,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"40","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Graham, Allie M.","contributorId":312459,"corporation":false,"usgs":false,"family":"Graham","given":"Allie","email":"","middleInitial":"M.","affiliations":[{"id":67676,"text":"Department of Human Genetics, University of Utah","active":true,"usgs":false}],"preferred":false,"id":876166,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jamison, Jerrica M.","contributorId":312460,"corporation":false,"usgs":false,"family":"Jamison","given":"Jerrica","email":"","middleInitial":"M.","affiliations":[{"id":67678,"text":"Department of Biological Sciences, University of Toronto","active":true,"usgs":false}],"preferred":false,"id":876167,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bustos, Marisol","contributorId":312461,"corporation":false,"usgs":false,"family":"Bustos","given":"Marisol","email":"","affiliations":[{"id":67679,"text":"Department of Biomedical Engineering, University of Texas","active":true,"usgs":false}],"preferred":false,"id":876168,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cournoyer, Charlotte","contributorId":312462,"corporation":false,"usgs":false,"family":"Cournoyer","given":"Charlotte","email":"","affiliations":[{"id":67680,"text":"South Florida Wildlife Center","active":true,"usgs":false}],"preferred":false,"id":876169,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Michaels, Alexa","contributorId":312463,"corporation":false,"usgs":false,"family":"Michaels","given":"Alexa","email":"","affiliations":[{"id":67681,"text":"Tufts University and The Jackson Laboratory","active":true,"usgs":false}],"preferred":false,"id":876170,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Presnell, Jason S.","contributorId":312464,"corporation":false,"usgs":false,"family":"Presnell","given":"Jason","email":"","middleInitial":"S.","affiliations":[{"id":67676,"text":"Department of Human Genetics, University of Utah","active":true,"usgs":false}],"preferred":false,"id":876171,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Richter, Rebecca","contributorId":207464,"corporation":false,"usgs":false,"family":"Richter","given":"Rebecca","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":876172,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crocker, Daniel E.","contributorId":202543,"corporation":false,"usgs":false,"family":"Crocker","given":"Daniel E.","affiliations":[{"id":36475,"text":"Sonoma State University","active":true,"usgs":false}],"preferred":false,"id":876173,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fustukjian, Ari","contributorId":312465,"corporation":false,"usgs":false,"family":"Fustukjian","given":"Ari","email":"","affiliations":[{"id":67682,"text":"Loveland Living Planet Aquarium","active":true,"usgs":false}],"preferred":false,"id":876174,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hunter, Margaret 0000-0002-4760-9302","orcid":"https://orcid.org/0000-0002-4760-9302","contributorId":214742,"corporation":false,"usgs":true,"family":"Hunter","given":"Margaret","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":876175,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rea, Lorrie D.","contributorId":82143,"corporation":false,"usgs":false,"family":"Rea","given":"Lorrie","email":"","middleInitial":"D.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":876176,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Marsillach, Judit","contributorId":207472,"corporation":false,"usgs":false,"family":"Marsillach","given":"Judit","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":876177,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Furlong, Clement E.","contributorId":207469,"corporation":false,"usgs":false,"family":"Furlong","given":"Clement","email":"","middleInitial":"E.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":876178,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Meyer, Wynn K.","contributorId":207462,"corporation":false,"usgs":false,"family":"Meyer","given":"Wynn","email":"","middleInitial":"K.","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":876179,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Clark, Nathan L.","contributorId":207470,"corporation":false,"usgs":false,"family":"Clark","given":"Nathan","email":"","middleInitial":"L.","affiliations":[{"id":12465,"text":"University of Pittsburgh","active":true,"usgs":false}],"preferred":false,"id":876180,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70243198,"text":"sir20235043 - 2023 - Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20","interactions":[],"lastModifiedDate":"2026-03-06T21:41:50.935249","indexId":"sir20235043","displayToPublicDate":"2023-05-04T12:49:40","publicationYear":"2023","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":"2023-5043","displayTitle":"Assessment of Conservation Management Practices on Water Quality and Observed Trends in the Plum Creek Basin, 2010–20","title":"Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20","docAbstract":"The U.S. Geological Survey and University of Wisconsin–Green Bay collected hydrologic and water-quality data to assess the effectiveness of agricultural conservation management practice (CMP) implementation at mainstem Plum Creek and west Plum Creek in northeastern Wisconsin. These two subbasins cover 88 percent of the Plum Creek Basin (Hydrologic Unit Code 12), which is a subbasin of the lower Fox River Basin. A published total maximum daily load report for the lower Fox River Basin rated Plum Creek as one of the greatest contributors of total suspended solids (TSS) and total phosphorus (TP) draining into the lower Fox River. To reduce TSS and TP exports from Plum Creek, additional cropland conservation practices and watercourse protections were applied between 2012 and 2020. To detect water-quality trends, data were collected during 2010 to 2020 at mainstem Plum Creek and 2013 to 2020 at west Plum Creek.
The project used two methods to evaluate CMP effectiveness. The first method focused on evaluating water-quality changes between initial and post-CMP implementation periods during rain- or snowmelt-induced runoff events (hereafter referred to as “events”). In this approach random-forest models were developed to account for environmental factors which influence water quality. Model residuals from the two time periods were compared to determine the significance of water-quality changes associated with CMP implementation for mainstem and west Plum Creek Basins. The second method used a Weighted Regressions on Time, Discharge, and Season time-series approach to examine changes in water quality during the entire study period in mainstem Plum Creek. Results from both methods indicated there were minimal water-quality changes in TSS concentrations and flow-normalized delivery during runoff events during the 10-year period from 2010 to 2020; however, TP concentrations during low streamflow (less than 3 cubic feet per second [ft3/s]) may have decreased. The lack of observed improvement may be attributable to any of the following: variability in weather and hydrologic conditions, insufficient post-treatment data, additional cropland being converted to corn production, above average rainfall, streambank degradation, acute and legacy sources of phosphorus from farm fields, excessive/vulnerable manure applications and spills, and point-source discharges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235043","collaboration":"Prepared in cooperation with the University of Wisconsin-Green Bay and Outagamie County, Wisconsin","usgsCitation":"Horwatich, J.A., Fermanich, K., Pronschinske, M.A., Robertson, D.M., Kussow, S., Loken, L.C., Reneau, P.C., Freund, J., and Komiskey, M.J., 2023, Assessment of conservation management practices on water quality and observed trends in the Plum Creek Basin, 2010–20: U.S. Geological Survey Scientific Investigations Report 2023–5043, 31 p., https://doi.org/10.3133/sir20235043.","productDescription":"Report: ix, 31 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-130579","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416705,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5043/coverthb.jpg"},{"id":416707,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":500920,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114718.htm","linkFileType":{"id":5,"text":"html"}},{"id":416709,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92A0H98","text":"USGS data release","linkHelpText":"Water quality and estimated changes in the Plum Creek watershed 2010–2020 (data release and model archive)"},{"id":416708,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5043/images"},{"id":416706,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5043/sir20235043.pdf","text":"Report","size":"8.23 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5043"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Plum Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.30723441433527,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.12306373303795\n ],\n [\n -87.89405155338416,\n 44.40323167054055\n ],\n [\n -88.30723441433527,\n 44.40323167054055\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
This poster uses photographs of scientists in action to introduce the principles of critical thinking and curiosity-driven science as they relate to the study of volcanoes. Captions align with educational “Next Generation Science Standards” and include job titles and tasks to increase career awareness among students and their teachers. The poster is available in both English and Spanish.
","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip212","usgsCitation":"Westby, E.G., and Faust, L.M., 2023, It begins with curiosity—How do scientists learn from volcanoes?: U.S. Geological Survey General Information Product 212, 1 plate, https://doi.org/10.3133/gip212.","productDescription":"2 Plates: 26.00 x 36.00 inches","onlineOnly":"N","ipdsId":"IP-124104","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":416724,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/212/covrthb.jpg"},{"id":416726,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/212/gip212_spanish.pdf","text":"Report","size":"8 MB Spanish","linkFileType":{"id":1,"text":"pdf"},"description":"Spanish"},{"id":416725,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/212/gip212_english.pdf","text":"Report","size":"8 MB English","linkFileType":{"id":1,"text":"pdf"},"description":"English"}],"contact":"Director,
Volcano Science Center
U.S. Geological Survey
1300 SE Cardinal Court
Vancouver, WA 38683
The Alaska Volcano Observatory responded to eruptions, considerable and minor volcanic unrest, and seismic events at 15 volcanic centers in Alaska during 2018. The most notable volcanic activity came from Mount Cleveland, which had continuing intermittent dome growth and ash eruptions, and Mount Veniaminof, Great Sitkin Volcano, and Semisopochnoi Island, the three of which had minor eruptions. This report also documents landslides at Iliamna Volcano; resuspended ash from the 1912 Novarupta-Katmai eruption; anomalous seismicity and heightened degassing at Pavlof Volcano; seismic unrest at Shishaldin Volcano; long-term inflation at Westdahl volcano, Akutan Volcano, and Mount Okmok; steam plumes, anomalous seismicity, and anomalous gas measurements at Makushin Volcano; elevated seismicity at Mount Gareloi; seismic signals possibly related to icequakes at Mount Spurr; and new mud flows at Shrub mud volcano.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235029","programNote":"The Alaska Volcano Observatory is a consortium between the U.S. Geological Survey, the University of Alaska Fairbanks Geophysical Institute, and the Alaska Division of Geological and Geophysical Surveys","usgsCitation":"Cameron, C.E., Orr, T.R., Dixon, J.P., Dietterich, H.R., Waythomas, C.F., Iezzi, A.M., Power, J.A., Searcy, C., Grapenthin, R., Tepp, G., Wallace, K.L., Lopez, T.M., DeGrandpre, K., and Perreault, J.M., 2023, 2018 Volcanic activity in Alaska—Summary of events and response of the Alaska Volcano Observatory: U.S. Geological Survey Scientific Investigations Report 2023–5029, 68 p., https://doi.org/10.3133/sir20235029.","productDescription":"vii, 68 p.","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-120153","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":500890,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114715.htm","text":"Mount Okmok; Mount Cleveland; Great Sitkin Volcano","linkFileType":{"id":5,"text":"html"}},{"id":500889,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114714.htm","text":"Westdahl Volcano; Akutan Volcano; Makushin Volcano","linkFileType":{"id":5,"text":"html"}},{"id":500891,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114716.htm","text":"Mount Gareloi; Mount Young; Semisopochnoi Volcano","linkFileType":{"id":5,"text":"html"}},{"id":500888,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114713.htm","text":"Mount Katmai; Mount Veniaminof; Pavlof Volcano","linkFileType":{"id":5,"text":"html"}},{"id":500887,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114712.htm","text":"Copper River Basin mud volcano; Mount Spurr; Iliamna Volcano","linkFileType":{"id":5,"text":"html"}},{"id":416689,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5029/sir20235029.pdf","text":"Report","size":"37 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416688,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5029/covrthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -143.20429710112703,\n 61.84436890827459\n ],\n [\n -142.33675036318309,\n 63.49574116221564\n ],\n [\n -146.4768151759031,\n 64.33396122050729\n ],\n [\n -150.9682913255116,\n 63.72196346359824\n ],\n [\n -153.61485795645464,\n 62.14462676432551\n ],\n [\n -160.86271677978394,\n 57.38330531702596\n ],\n [\n -168.6376926084463,\n 54.5268905469634\n ],\n [\n -177.81831378466347,\n 52.19951793188412\n ],\n [\n -178.25757795577434,\n 51.654015133859275\n ],\n [\n -176.93978544244175,\n 51.32665962050979\n ],\n [\n -169.5601473677791,\n 52.146191529316866\n ],\n [\n -156.55792790289746,\n 54.975513752431766\n ],\n [\n -151.28675784956704,\n 57.477711052577945\n ],\n [\n -146.718410470014,\n 60.74058307242174\n ],\n [\n -143.20429710112703,\n 61.84436890827459\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
U.S. Geological Survey
4210 University Drive
Anchorage, AK 99508
The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, carries out research into magmatic processes occurring beneath Yellowstone Caldera, and issues timely warnings and guidance related to potential future geologic hazards. This report summarizes the activities and findings of YVO during the year 2022, focusing on the Yellowstone volcanic system. Highlights of YVO research and related activities during 2022 include deployments of seismometers in Norris Geyser Basin and Upper Geyser Basin to investigate interactions between hydrothermal features and influences from external influences, geological studies of post-glacial hydrothermal activity, refining the ages of Yellowstone volcanic units and updating existing maps of geologic deposits, new mapping of ash-flow deposits on the Sour Creek dome, installation of a new continuous gas monitoring station near Mud Volcano, sampling of gas emissions and thermal waters around Yellowstone National Park to monitor water chemistry over space and time, research into the age and history of Steamboat Geyser in Norris Geyser Basin, and assessment of thermal output based on satellite imagery and chloride flux in rivers.
The most noteworthy event of the year was not geophysical, but meteorological. Combined runoff from rain and snowmelt caused substantial flooding in Yellowstone National Park, which caused damage to park roads and infrastructure. Steamboat Geyser, in Norris Geyser Basin, continued the pattern of frequent eruptions that began in 2018 with 11 water eruptions in 2022, the lowest number of annual eruptions in the current eruptive sequence. Total seismicity—2,429 located earthquakes—was slightly less than the 2,773 earthquakes located in 2021 and at the upper end of the historical average range of about 1,500–2,500 earthquakes per year. Overall subsidence of the caldera floor, ongoing since late 2015 or early 2016, continued at rates of a few centimeters (1–2 inches) per year. Satellite deformation measurements indicated the possibility of slight uplift amounting to about 1 centimeter (less than 1 inch) along the north caldera rim in 2021, but satellite data spanning 2022 show no uplift in that area. Throughout 2022, the aviation color code for Yellowstone Caldera remained at “green” and the volcano alert level remained at “normal.”
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1508","usgsCitation":"Yellowstone Volcano Observatory, 2023, Yellowstone Volcano Observatory 2022 annual report: U.S. Geological Survey Circular 1508, 49 p., https://doi.org/10.3133/cir1508.","productDescription":"v, 49 p.","numberOfPages":"49","onlineOnly":"N","ipdsId":"IP-149199","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":416682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1508/covrthb.jpg"},{"id":416683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1508/cir1508.pdf","text":"Report","size":"28 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499548,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114711.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.22714973143812,\n 45.10797687707381\n ],\n [\n -111.22714973143812,\n 43.34546446716831\n ],\n [\n -108.61352791332872,\n 43.34546446716831\n ],\n [\n -108.61352791332872,\n 45.10797687707381\n ],\n [\n -111.22714973143812,\n 45.10797687707381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Yellowstone Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Suite 100
Vancouver, WA 98683
Email: yvowebteam@usgs.gov
","tableOfContents":"To help support remedial efforts at the former Badger Army Ammunition Plant the U.S. Geological Survey built and calibrated a transient groundwater flow model using the Newton Raphson formulation (MODFLOW–NWT) of the U.S. Geological Survey’s modular three-dimensional finite-difference code. The model simulates the groundwater flow system at the site from 1984 to 2020. The former Badger Army Ammunition Plant is a 7,275-acre site in Sauk County, Wisconsin. The plant produced smokeless gunpower and solid rocket propellent as munitions components. Peak production periods were during World War II, the Korean War, and the Vietnam War. Subsequent groundwater contamination investigations have found four plumes at the site. A health risk assessment identified at least one contaminant of concern for human health risk present in three of the plumes: the propellant burning ground plume, the deterrent burning ground plume, and the central plume. A cooperative study began between the U.S. Army Environmental Command and U.S. Geological Survey to better understand the groundwater flow system at the former Badger Army Ammunition Plant. Field data, including aquifer tests, streamflow measurements, continuous groundwater elevations, and groundwater gradients with the Wisconsin River were collected and used to inform and calibrate the groundwater flow model. The model was used to assess the variability of the groundwater system over the study period, the components of the groundwater budget, and groundwater flow directions from identified source areas towards the Wisconsin River. Model performance assessment focused on using particle tracking to compare groundwater flowpaths that originate in the contaminant source areas to the observed plume footprints. This focus on plume behavior geometry should help constrain the advective component of a future groundwater transport model of the site.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235040","collaboration":"Prepared in cooperation with U.S. Army Environmental Command","usgsCitation":"Haserodt, M.J., Reeves, H.W., Nielsen, M.G., Schachter, L.A., Corson-Dosch, N.T., and Feinstein, D.T., 2023, Simulation of groundwater flow at the former Badger Army Ammunition Plant, Sauk County, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2023–5040, 140 p., https://doi.org/10.3133/sir20235040.","productDescription":"Report: viii, 140 p.; 3 Data Releases; Dataset","numberOfPages":"152","onlineOnly":"Y","ipdsId":"IP-135445","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":416676,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S2IDV0","text":"USGS data release","linkHelpText":"Soil-Water-Balance (SWB) model archive used to simulate potential annual recharge for the former Badger Army Ammunition Plant study area, Prairie du Sac, Wisconsin, 1980 to 2020"},{"id":416672,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":416671,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5040/sir20235040.pdf","text":"Report","size":"106 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023–5040"},{"id":416670,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5040/coverthb.jpg"},{"id":500916,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114707.htm","linkFileType":{"id":5,"text":"html"}},{"id":416678,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":416675,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95TSI73","text":"USGS data release","linkHelpText":"Slug test analysis results from unconsolidated and bedrock aquifers at Badger Army Ammunition Plant, Sauk County, Wisconsin, 2020"},{"id":416674,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5040/images"},{"id":416677,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LNRILT","text":"USGS data release","linkHelpText":"Groundwater model archive for the former Badger Army Ammunition Plant, Wisconsin"},{"id":416712,"rank":9,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235040/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wisconsin","county":"Sauk County","otherGeospatial":"former Badger Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.76922975135027,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.33005054374769\n ],\n [\n -89.70231568538874,\n 43.38657213852542\n ],\n [\n -89.76922975135027,\n 43.38657213852542\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
1 Gifford Pinchot Drive
Madison, WI 53726
Previously glaciated landscapes often share similar surficial characteristics, including large areas of exposed bedrock, blankets of till deposits, and alluvium-floored valleys. These materials play significant roles in geologic and hydrologic resources, geohazards, and landscape evolution; however, the vast extents of many previously glaciated landscapes have rendered comprehensive, detailed field mapping difficult. While recent advances in remote sensing have facilitated mapping of surficial materials and landforms, manual map creation has remained a time-intensive task.
The development of convolutional neural networks (CNNs) for image classification has provided a new opportunity for rapid characterization of digital elevation models, thus enabling efficient mapping of surficial materials and landforms. We have developed a methodology that leverages existing geologic maps and high-resolution (1–3 m) lidar data to train a U-Net CNN to classify alluvium and exposed bedrock in previously glaciated regions. Coupled with U.S. Geological Survey-developed geomorphometry tools capable of approximating stream incision depths, these classifications can be used to estimate the minimum thicknesses of stream-proximal hillslope sediments in areas where streams have undergone minimal incision into bedrock.
We validate this approach in the context of the Neversink River watershed, a subbasin of the Delaware River Basin and significant water source for New York City. Evaluation of deep learning model performance demonstrates substantial agreement with manually drawn maps of alluvium and exposed bedrock. Validation of the minimum sediment thickness map using borehole data and passive seismic measurements shows the greatest performance for shallow materials and decreased performance in deep sediments, as well as in areas where bedrock exposures were too small to be resolved by lidar. To resolve these issues and create more accurate surficial maps, we are training new CNNs with additional geologic data and exploring advanced approaches for estimating depths of stream incision.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.acags.2023.100116","usgsCitation":"Odom, W.E., and Doctor, D.H., 2023, Rapid estimation of minimum depth-to-bedrock from lidar leveraging deep-learning-derived surficial material maps: Applied Computing and Geosciences, v. 18, 100116, 11 p., https://doi.org/10.1016/j.acags.2023.100116.","productDescription":"100116, 11 p.","ipdsId":"IP-146769","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443651,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.acags.2023.100116","text":"Publisher Index Page"},{"id":417128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.31774531759645,\n 38.65648371068133\n ],\n [\n -74.83711116001508,\n 39.00784172038104\n ],\n [\n -74.58034795534637,\n 39.29641683375996\n ],\n [\n -74.84560023131485,\n 39.59878756424641\n ],\n [\n -74.57402157309659,\n 39.81435898583538\n ],\n [\n -74.36164212699806,\n 40.42339954973025\n ],\n [\n -74.7398677925508,\n 40.67483290305228\n ],\n [\n -74.20311730788073,\n 41.49081439956416\n ],\n [\n -73.87900349307475,\n 42.134764710063195\n ],\n [\n -74.40404082594178,\n 42.53787114018144\n ],\n [\n -75.19513563542162,\n 42.478156559974536\n ],\n [\n -75.74165548596034,\n 41.860885020202005\n ],\n [\n -76.19243039302005,\n 41.151909429148475\n ],\n [\n -76.53910969789781,\n 40.49637767696336\n ],\n [\n -76.22016361968275,\n 40.00185213900514\n ],\n [\n -75.6922644432239,\n 39.71427259376151\n ],\n [\n -75.61094038798961,\n 39.383028287456796\n ],\n [\n -75.31774531759645,\n 38.65648371068133\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Odom, William E. 0000-0001-8577-5056","orcid":"https://orcid.org/0000-0001-8577-5056","contributorId":292616,"corporation":false,"usgs":true,"family":"Odom","given":"William","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":872923,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70244178,"text":"70244178 - 2023 - Bringing the Nature Futures Framework to life: Creating a set of illustrative narratives of nature futures","interactions":[],"lastModifiedDate":"2023-06-06T11:51:36.740586","indexId":"70244178","displayToPublicDate":"2023-05-04T06:49:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5318,"text":"Sustainability Science","active":true,"publicationSubtype":{"id":10}},"title":"Bringing the Nature Futures Framework to life: Creating a set of illustrative narratives of nature futures","docAbstract":"To halt further destruction of the biosphere, most people and societies around the globe need to transform their relationships with nature. The internationally agreed vision under the Convention of Biological Diversity—Living in harmony with nature—is that “By 2050, biodiversity is valued, conserved, restored and wisely used, maintaining ecosystem services, sustaining a healthy planet and delivering benefits essential for all people”. In this context, there are a variety of debates between alternative perspectives on how to achieve this vision. Yet, scenarios and models that are able to explore these debates in the context of “living in harmony with nature” have not been widely developed. To address this gap, the Nature Futures Framework has been developed to catalyse the development of new scenarios and models that embrace a plurality of perspectives on desirable futures for nature and people. In this paper, members of the IPBES task force on scenarios and models provide an example of how the Nature Futures Framework can be implemented for the development of illustrative narratives representing a diversity of desirable nature futures: information that can be used to assess and develop scenarios and models whilst acknowledging the underpinning value perspectives on nature. Here, the term illustrative reflects the multiple ways in which desired nature futures can be captured by these narratives. In addition, to explore the interdependence between narratives, and therefore their potential to be translated into scenarios and models, the six narratives developed here were assessed around three areas of the transformative change debate, specifically, (1) land sparing vs. land sharing, (2) Half Earth vs. Whole Earth conservation, and (3) green growth vs. post-growth economic development. The paper concludes with an assessment of how the Nature Futures Framework could be used to assist in developing and articulating transformative pathways towards desirable nature futures.
Water availability is a result of complex interactions between regional water supply and demand and underlying environmental, institutional, and economic determinants. For this study, water availability is defined as “access to a specific quantity and quality of water at a point in time and space, for a specific use, recognizing the social and economic value of water across uses and institutions that facilitate or hinder its equitable and efficient provisioning.” This report identifies the human factors that influence water supply and demand and summarizes (1) the extensive sets of data available to estimate these factors in the agricultural, municipal, and industrial water-use sectors and (2) factors of recreation and ecosystem services that influence water availability in the Upper Colorado River Basin. Lastly, future research needs are identified that can help prioritize collection and refinement of human factors of water use to improve water availability estimation and forecasting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235015","usgsCitation":"Herman-Mercer, N., Bair, L., Hines, M., Restrepo-Osorio, D., Romero, V., and Lyde, A., 2023, Human factors used to estimate and forecast water supply and demand in the Upper Colorado River Basin: U.S. Geological Survey Scientific Investigations Report 2023–5015, 46 p., https://doi.org/10.3133/sir20235015.","productDescription":"Report: v, 46 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-134554","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":416657,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99PAIVH","text":"USGS data release","linkHelpText":"Human Factors of Water Availability in the Upper Colorado River Basin"},{"id":416667,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5015/sir20235015.xml"},{"id":416666,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5015/images"},{"id":415708,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5015/coverthb.jpg"},{"id":416656,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5015/sir20235015.pdf","text":"Report","size":"23.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5015"},{"id":416903,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235015/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5015"},{"id":500709,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114654.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.10551489403292,\n 42.12593624912847\n ],\n [\n -112.14876730042165,\n 40.578643397004406\n ],\n [\n -112.4013441988101,\n 38.62958426227543\n ],\n [\n -112.47821542875455,\n 36.49853660965043\n ],\n [\n -111.23729414536629,\n 34.720565339434735\n ],\n [\n -109.59005350370012,\n 33.6922933450013\n ],\n [\n -107.63532794225712,\n 33.832340766660366\n ],\n [\n -106.88857885136851,\n 35.06376781338098\n ],\n [\n -106.31753542892439,\n 37.332120502512296\n ],\n [\n -106.44931468025776,\n 39.33043034353207\n ],\n [\n -107.59140152514598,\n 41.43808608645108\n ],\n [\n -108.03066569625673,\n 42.12593624912847\n ],\n [\n -109.69986954647847,\n 42.935426421776896\n ],\n [\n -110.57839788869994,\n 42.74193975501615\n ],\n [\n -111.10551489403292,\n 42.12593624912847\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Integrated Information Dissemination Division
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The National Park Service (NPS), in collaboration with the U.S. Geological Survey (USGS), recognizes the need to quantify the sediment budget of the barrier islands within the Gulf Islands National Seashore (GINS) to understand the coastal processes affecting island resiliency. To achieve this goal, identifying and quantifying the physical parameters that drive long-term change is necessary to model the processes that are both generative and terminal in island evolution and capture island response to long-term human alteration and climatic patterns. For example, measuring change across periods of storminess is more effective at assessing island resiliency than measuring change resulting from a single storm impact. Understanding changes to the physical environment over time is key to successfully predicting island responses to future storm impacts, human alteration, and sea-level rise and is necessary for effective decision making and management response. Yet, the diversity of factors affecting natural and cultural resources necessitates a strategic approach to data collection priorities that can inform sediment budget quantification and integrated resource management.
This study sought to advance sediment budget modeling efforts by conducting a “Needs Assessment Workshop” at the GINS. The purpose of the workshop was to identify and prioritize the specific research and data needs regarding the sediment budget at the GINS that can enhance the NPS efforts to conserve the islands’ natural resources, cultural resources, and the facilities and infrastructure that support both conservation and visitor use of those resources. This effort explored two research questions: (1) “what research and data needs exist for the sediment budget at Gulf Islands National Seashore” (research question 1) and (2) “how can research to address these needs capitalize on regional partnerships to advance natural and cultural resource conservation at Gulf Islands National Seashore” (research question 2)? The workshop was conducted virtually in a two-part, two-day series.
The workshop series was organized by researchers from North Carolina State University in collaboration with NPS and USGS staff and was facilitated by National Oceanographic and Atmospheric Administration staff. The workshop series (two paired, sequential, partial-day workshops) addressed two target audiences: (1) NPS and USGS staff (April workshop) and (2) regional Federal, State, county, and nongovernmental organization staff, including NPS and USGS staff (May workshop). A total of four workshop sessions were held, comprising two sessions with each target audience.
The workshop series intended to identify sediment management research and data needs that could enhance natural and cultural resource stewardship at the GINS. One objective was to share information about regional sediment transport and management, available sediment management plans, and predictive modeling capabilities, including geomorphologic and hydrodynamic predictive models. This information was shared through a series of presentations by park managers and NPS and USGS researchers that identified park issues and available capabilities and data. The second objective was to elicit research and data needs, with a primary goal of assessing the importance and urgency of the identified needs. This assessment was partly determined by requesting that the workshop participants identify and prioritize research themes through polls, comments, and discussion.
The polls explicitly asked participants to qualitatively evaluate the importance (not at all, slightly, somewhat, very, or extremely) and urgency (not at all, slightly, somewhat, very, or extremely) of the thematically grouped research and data needs. These evaluations were plotted and shared during the workshop to visualize how the relative importance (x-axis) and relative urgency (y-axis) of each “need,” relative to other needs, to identify the most necessary (importance) and time-sensitive (urgent) items, thereby allowing an enhanced, holistic understanding of the sediment budget at GINS. Results of the poll are published as a USGS data release.
The assessment results revealed that the most important and urgent research and data needs included mapping (for example, elevation, habitat, and cultural resources), a regional sediment budget and management plan, and the dynamic modeling of sediment processes. During the workshop, these issues were visualized using scatter plots to demonstrate the relative importance and urgency of each theme, provide descriptive statistics, and elicit discussion. This format of iterative presentation, discussion, and prioritization allowed the project team to effectively accomplish their objective of identifying important and urgent research needs for natural and cultural resource stewardship at the GINS. Through the workshop, it was determined that expanded communication with the broader research community was needed to coordinate research activities and streamline potential funding opportunities and that research and policy should be integrated through a structured decision-making process.
At the conclusion of the workshop, an administered poll showed that the presentations effectively identified data and research needs and that the goals of the workshop were achieved. The results suggest that this type of needs-assessment workshop can effectively identify existing research capabilities and data, determine and prioritize research and data needs, and address how these efforts can use regional partnerships to aid natural and cultural resource conservation and management at National Parks.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221087","programNote":"Coastal/Marine Hazards and Resources Program","usgsCitation":"Seekamp, E., Flocks, J., Hotchkiss, C., York, L., and Irick, K., 2023, Gulf Islands National Seashore regional sediment budget research and data needs—Workshop series summary: U.S. Geological Survey Open-File Report 2022–1087, 46 p., https://doi.org/10.3133/ofr20221087.","productDescription":"Report: vii, 46 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127837","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":416372,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1087/coverthb.jpg"},{"id":416373,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1087/ofr20221087.pdf","text":"Report","size":"2.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1087"},{"id":416375,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1087/ofr20221087.XML"},{"id":416376,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1087/images/"},{"id":416377,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JG3J7B","text":"USGS data release","linkHelpText":"Gulf Islands National Seashore 2020 workshop-attendee survey results"},{"id":499720,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114708.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida, Mississippi","otherGeospatial":"Gulf Islands National Seashore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.47993451563534,\n 30.4372165405142\n ],\n [\n -89.47993451563534,\n 30.14326231135827\n ],\n [\n -86.39410371358161,\n 30.14326231135827\n ],\n [\n -86.39410371358161,\n 30.4372165405142\n ],\n [\n -89.47993451563534,\n 30.4372165405142\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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Although freshwater mussels are imperiled and identified as key conservation priorities, limited bioaccumulation information is available on these organisms for contaminants of emerging concern. In the present study we investigated the bioaccumulation of per- and polyfluoroalkyl substances (PFAS) in the model freshwater pond mussel Sagittunio subrostratus because mussels provide important ecosystem services and are important components of aquatic systems where PFAS occur. In the present study we selected four representative perfluorinated carboxylic acids and sulfonic acids, then determined the bioaccumulation kinetics of freshwater mussels in a controlled laboratory study. Because uptake (ku) and elimination (ke) rate constants and time to steady state are important parameters for food web bioaccumulation models, we derived bioaccumulation kinetic parameters following exposure to perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonic acid (PFOS), and perfluorodecanoic acid (PFDA) at 10 µg/L and perfluoroundecanoic acid (PFUnDA) at 1 µg/L during a 14-day uptake period followed by a 7-day elimination period. Kinetic and ratio-based bioaccumulation factors (BAFs) were subsequently calculated, for example ratio-based BAFs for mussel at day 7 were determined for PFHxS (0.24 ± 0.08 L/kg), PFOS (7.73 ± 1.23 L/kg), PFDA (4.80 ± 1.21 L/kg), and PFUnDA (84.0 ± 14.4 L/kg). We generally observed that, for these four model PFAS, freshwater mussels have relatively low BAF values compared with other aquatic invertebrates and fish.
Iron-sulfide minerals found in shale formations are stable under anaerobic conditions. However, in the presence of oxygen and water, acid-loving chemolithotrophic bacteria can transform the iron-sulfide minerals into a toxic solution of sulfuric acid and dissolved iron and minerals known as acid rock drainage (ARD). The objective of this study was to disrupt chemolithotrophic bacteria responsible for ARD using chemical treatments and to foster an environment favorable for competing microorganisms to attenuate the biologically induced ARD. Chemical treatments were injected into flow-through microcosms consisting of 501 g of pyrite-rich shale pieces inoculated with ARD bacteria. Three treatments were tested in the microcosms: (1) a sodium hydroxide-bleach mix, (2) a sodium lactate solution, and (3) a sodium lactate-soy infant formula mix. The effectiveness of the treatments was assessed by monitoring pH, dissolved iron, and other geochemical constituents in the discharge waters. The optimal treatment was a sequential injection of 1.5 g sodium hydroxide, followed by 0.75 g lactate and 1.5 g soy formula dissolved in 20 mL water. The pH of the discharge water rose to 6.0 within 10 days, dissolved iron concentrations dropped below 1 mg/L, the median alkalinity increased to 98 mg/L CaCO3, and sulfur-reducing and slime-producing bacteria populations were stimulated. The ARD attenuating benefits of this treatment were still evident after 231 days. Other treatments provided a number of ARD attenuating effects but were tempered by problems such as high phosphate concentrations, short longevity, or other shortcomings. The results of these laboratory microcosm experiments were promising for the attenuation of ARD. Additional investigations and careful selection of treatment methods will be needed for field application.
Groundwater in the karst groundwater system at Area B of Fort Detrick in Frederick County, Maryland, is contaminated with chlorinated solvents from the past disposal of laboratory wastes. In cooperation with U.S. Army Environmental Command and U.S. Army Garrison Fort Detrick, the U.S. Geological Survey performed a 3-year study to refine the conceptual model of groundwater flow in and around Area B of Fort Detrick at the site- to regional-scale. The investigation was designed to review the geologic setting, assess the temporal variability of the hydrologic system, evaluate the potential for interbasin groundwater flow, determine the degree of vertical connectivity of the aquifer, characterize the sources and timing of groundwater recharge, and identify if dyes from previous tracer tests continue to drain from the aquifer. This study established a continuous hydrologic monitoring network of 12 water level gages, 2 streamgages, a precipitation gage, and in situ fluorometric monitoring. A water budget analysis was performed using hydrologic monitoring data and a soil-water balance model constructed for the study. In this study each individual water budget term is calculated using available data or through modeling, and a water budget residual term is calculated. If the water budget residual term is small relative to the uncertainty of the underlying data, then an additional import or export of water (in other words, interbasin transfer) is not needed to fully describe the hydrologic system. Groundwater and spring samples from 20 locations were collected in a 2019 synoptic geochemical sampling event and analyzed for a suite of analytes that included groundwater age tracer constituents.
The karst groundwater system was found to be highly responsive to hydrologic events, with strong water level and stream base flow responses to individual storm events and a historic wet period in 2017 and 2018. The water budget analysis included historic flooding in May 2018, though more typical hydrologic patterns were observed in 2019 and 2020. During most evaluated intervals, the water budget residual was less than the estimated uncertainty on the residual for the two Carroll Creek watersheds, which suggested no substantial net interbasin flow occurs from these watersheds. The watershed difference area, a region that includes Area B, had a significant negative water budget residual, which may be the result of a net interbasin import of groundwater or the result of focused groundwater recharge not simulated by the soil-water balance model. Geochemical analysis and groundwater age dating reveals shallow groundwater (approximately less than [<] 150 feet deep) appears to be relatively young (approximately <30 years) and to be recharged in the vicinity of Area B. In the deep groundwater sampled in this study (approximately greater than [>] 150 feet deep), older groundwater from a differing recharge source, based on stable isotopes and noble gas analyses, is observed and interpreted to represent less direct connectivity to the surface and increased proportions of water recharged to the north and (or) west of Area B. A clustering analysis to reveal groupings within the suite of geochemical data was used to define seven groups. The groupings generally show that wells in similar depths and lateral aquifer positions generally cluster together, with some exceptions. Although limited by suspended sediments, the in situ fluorometric monitoring at springs did not detect any dye leaving the system above the limit of detection for the method. Dye was only detected above the limit of detection in one well, which was used as an injection well during a previous dye tracer test.
The results of this study support and refine the conceptual site model of groundwater hydrology at Area B. The geologic and geophysical log review in this study agrees with prior assessments of physical controls on groundwater flow. A literature review of mid-Atlantic karst studies identified similar controls reported in these environments. The additional characterization of hydrologic responsiveness in this study suggests that hydrologic conditions and events are important considerations when interpreting potentiometric surfaces and contaminant trends over time and highlights the importance of continuous hydrologic monitoring. There is evidence to suggest that either intense focused groundwater recharge occurs in the vicinity of Area B or net along-valley groundwater interbasin flow from the upper study watershed enters the lower watershed and discharges to Carroll Creek. Geochemical analyses also suggest that water recharged from Catoctin Mountain and the elevated areas to the north and (or) west of the site may be present in the older and deeper Area B groundwater.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225054","collaboration":"Prepared in cooperation with U.S. Army Environmental Command and U.S. Army Garrison, Fort Detrick","usgsCitation":"Goodling, P.J., Fleming, B.J., Solder, J., Soroka, A., and Raffensperger, J., 2023, Hydrogeologic characterization of Area B, Fort Detrick, Maryland: U.S. Geological Survey Scientific Investigations Report 2022–5054, 128 p., https://doi.org/10.3133/sir20225054.","productDescription":"Report: xiv, 128 p.; 2 Data Releases","numberOfPages":"128","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-124092","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":435349,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DUFZY7","text":"USGS data release","linkHelpText":"Supporting Datasets for Hydrogeological Characterization of Ft. Detrick Area B, Maryland"},{"id":500936,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114709.htm","linkFileType":{"id":5,"text":"html"}},{"id":416517,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GTTX8Q","text":"USGS data release","linkHelpText":"Soil water balance model developed for Maryland and Pennsylvania"},{"id":416516,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AYWBXU","text":"USGS data release","linkHelpText":"Supporting datasets for hydrogeological characterization of Area B, Fort Detrick, Maryland"},{"id":416515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.pdf","text":"Report","size":"51.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5054"},{"id":416514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5054/coverthb.jpg"},{"id":416562,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225054/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5054"},{"id":416564,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5054/images/"},{"id":416563,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5054/sir20225054.XML"}],"country":"United States","state":"Maryland","otherGeospatial":"Fort Detrick","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.4693386578843,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.41628758896462\n ],\n [\n -77.38630852298522,\n 39.458154924593\n ],\n [\n -77.4693386578843,\n 39.458154924593\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
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A 2,000 year-long oceanographic history, in sub-centennial resolution, from a Canadian Beaufort Sea continental shelf site (60meters water depth) near the Mackenzie River outlet is reconstructed from ostracode and foraminifera faunal assemblages, shell stable isotopes (delta 18O, delta 13C) and sediment biogenic silica. The chronology of three sediment cores making up the composite section was established using 137Cs and 210Pb dating for the most recent 150 years and combined with linear interpolation of radiocarbon dates from bivalve shells and foraminifera tests.Continuous centimeter-sampling of the multicore and high-resolution sampling of a gravity and piston core yielded a time-averaged faunal record of every approximately 40 years from 0 to 1850 CE and every approximately 24 years from 1850 to 2013 CE. Proxy records were consistent with temperature oscillations and related changes in organic carbon cycling associated with the Medieval Climate Anomaly (MCA) and the Little Ice Age (LIA). Abundance changes in dominant microfossil species, such as the ostracode Paracyprideis pseudopunctillata and agglutinated foraminifers Spiroplectammina biformis and S. earlandi, are used as indicators of less saline, and possibly corrosive/turbid bottom conditions associated with the MCA (approximately 800 to 1200 CE) and the most recent approximately 60 years (1950–2013). During these periods, pronounced fluctuations in these species suggest that prolonged seasonal sea-ice melting, changes in riverine inputs and sediment dynamics affected the benthic environment. Taxa analyzed for stable oxygen isotope composition of carbonates show the lowest delta 18O values during intervals within the MCA and the highest during the late LIA, which is consistent with a 1 degree to 2 degree C cooling of bottom waters. Faunal and isotopic changes during the cooler LIA (1300 to 1850 CE) are most apparent at approximately 1500 to 1850 CE and are particularly pronounced during 1850 to approximately 1900 CE, with an approximate 0.5 per mil increase in delta 18O values of carbonates from median values in the analyzed taxa. This very cold 50-year period suggests that enhanced summer sea ice suppressed productivity,which is indicated by low sediment biogenic silica values and lower delta 13C values in analyzed species. From 1900CE to present, declines in calcareous faunal assemblages and changes in dominant species (Cassidulina reniforme and P. pseudopunctillata) are associated with less hospitable bottom waters, indicated by a peak in agglutinated foraminifera from 1950 to 1990 CE.
","language":"English","publisher":"Micropaleontology Press","doi":"10.47894/mpal.69.3.04","usgsCitation":"Gemery, L., Cronin, T.M., Cooper, L.W., Roberts, L., Keigwin, L., Addison, J.A., Leng, M., Lin, P., Magen, C., Marot, M.E., and Schwartz, V., 2023, Multi-proxy record of ocean-climate variability during the last 2 millennia on the Mackenzie Shelf, Beaufort Sea: Micropaleontology, v. 69, no. 3, p. 345-366, https://doi.org/10.47894/mpal.69.3.04.","productDescription":"22 p.","startPage":"345","endPage":"366","ipdsId":"IP-134578","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443665,"rank":3,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nora.nerc.ac.uk/id/eprint/535369/1/33670_articles_article_file_2322.pdf","text":"External Repository"},{"id":435351,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SRRW6T","text":"USGS data release","linkHelpText":"Data Release to Multi-proxy record of ocean-climate variability during the last 2 millennia on the Mackenzie Shelf, Beaufort Sea (2013)"},{"id":416619,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Beaufort Sea, Mackenzie Shelf","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -130.57152373008722,\n 71.44885475594037\n ],\n [\n -144.9141394593442,\n 71.44885475594037\n ],\n [\n -144.9141394593442,\n 68.59098357271469\n ],\n [\n -130.57152373008722,\n 68.59098357271469\n ],\n [\n -130.57152373008722,\n 71.44885475594037\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"69","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Gemery, Laura 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Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":871360,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Magen, Cedric","contributorId":265132,"corporation":false,"usgs":false,"family":"Magen","given":"Cedric","email":"","affiliations":[{"id":54603,"text":"University of Maryland Center for Environmental Science, Chesapeake Biological Lab, Solomons MD","active":true,"usgs":false}],"preferred":false,"id":871361,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marot, Marci E. 0000-0003-0504-315X mmarot@usgs.gov","orcid":"https://orcid.org/0000-0003-0504-315X","contributorId":2078,"corporation":false,"usgs":true,"family":"Marot","given":"Marci","email":"mmarot@usgs.gov","middleInitial":"E.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":871362,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schwartz, Valerie 0000-0003-2874-8435","orcid":"https://orcid.org/0000-0003-2874-8435","contributorId":279845,"corporation":false,"usgs":false,"family":"Schwartz","given":"Valerie","affiliations":[{"id":57375,"text":"Juul","active":true,"usgs":false}],"preferred":false,"id":871363,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70243022,"text":"sir20235023 - 2023 - Sediment transport in two tributaries to the San Joaquin River immediately below Friant Dam—Cottonwood Creek and Little Dry Creek, California","interactions":[],"lastModifiedDate":"2026-03-06T20:43:31.136709","indexId":"sir20235023","displayToPublicDate":"2023-05-02T08:31:13","publicationYear":"2023","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":"2023-5023","displayTitle":"Sediment Transport in Two Tributaries to the San Joaquin River Immediately Below Friant Dam—Cottonwood Creek and Little Dry Creek, California","title":"Sediment transport in two tributaries to the San Joaquin River immediately below Friant Dam—Cottonwood Creek and Little Dry Creek, California","docAbstract":"Two tributaries to the greater San Joaquin River watershed, Cottonwood and Little Dry Creeks, in California’s Central Valley, were assessed for sediment and streamflow dynamics between October 1, 2011, and September 30, 2019. The two systems deliver sediment to the San Joaquin River below Friant Dam, California. Dams create downstream discontinuities in streamflow and sediment transport and therefore influence fish habitat and sediment dynamics. Because these two creeks are directly downriver from Friant Dam, they become the most upstream source of sediment to the San Joaquin River below Friant Dam.
The quality and quantity of spawning habitat for fish in the gravel-bedded reach of the San Joaquin River relies on a range of bed material particle size suitable for redd structure. The effects of coarse-sand to fine-gravel supply on salmonid habitat depends primarily on the size of the sediment and the timing of its addition from tributaries to the San Joaquin River; thus, understanding the timing, quantity, and size of sediment supplied from these two tributaries is critical to the management of ecological and biological sustainability.
Streamflow from Cottonwood and Little Dry Creeks, along with streamflow from the San Joaquin River below Friant Dam, were compared to continuously measured water-surface elevations to quantify the timing and direction of streamflow. Suspended-sediment samples were collected with multiple automatic samplers and analyzed for concentration and grain-size distribution. Measured suspended-sediment concentrations and streamflows were used to develop sediment rating curves and compute continuous estimates of suspended-sediment load for each tributary. Satellite imagery was used to qualify spatial and temporal dynamics through the lower watersheds and support more quantitative sediment-load estimates.
Computed annual sediment loads ranged from 1.32x101 to 2.68x104 metric tons for Little Dry Creek and 9.82 to 1.98x103 metric tons for Cottonwood Creek. Sediment loads computed during the study period for both watersheds show that annual loads were highest during water year 2017 (October 1, 2016, to September 30, 2017). Sediment transport primarily occurred between the months of January and March. In both tributaries, grain-size distributions of suspended sediment were predominantly coarse-sized sand and were finer than the remnant bed material.
Both creeks demonstrate backwater effects from the San Joaquin River, but the more tortuous stream channel and historical mining pits within Little Dry Creek provide more capacity for sediment storage compared to the less complex stream network of Cottonwood Creek. Because loads were computed based on upstream streamgages and not at the confluence of each tributary to the San Joaquin River, annual load estimates do not represent direct flux into the San Joaquin River; instead, these results indicated that in Little Dry Creek, particularly, the lowest portion of the watershed stores sediment before it reaches the San Joaquin River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235023","collaboration":"Prepared in cooperation with the Bureau of Reclamation San Joaquin River Restoration Program","programNote":"National Water Quality Program and Water Availability and Use Science Program","usgsCitation":"Haught, D.R.W., Marineau, M.D., Minear, J.T., Wright, S.A., and Lopez, J.V., 2023, Sediment transport in two tributaries to the San Joaquin River immediately below Friant Dam—Cottonwood Creek and Little Dry Creek, California: U.S. Geological Survey Scientific Investigations Report 2023–5023, 34 p., https://doi.org/10.3133/sir20235023.","productDescription":"Report: ix, 34 p.; Data Release","numberOfPages":"34","ipdsId":"IP-120407","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":416394,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5023/images"},{"id":416392,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5023/sir20235023.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":416391,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5023/covrthb.jpg"},{"id":500875,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114705.htm","linkFileType":{"id":5,"text":"html"}},{"id":416396,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E1OYNM","text":"Little Dry Creek and Cottonwood Creek sediment transport data, 2012–2018, San Joaquin Watershed in the California Central Valley","description":"Haught, D.R.W., and Marineau, M.D., 2023, Little Dry Creek and Cottonwood Creek sediment transport data, 2012–2018, San Joaquin Watershed in the California Central Valley: U.S. Geological Survey data release, https://doi.org/10.5066/P9E1OYNM."}],"country":"United States","state":"California","otherGeospatial":"Little Dry Creek and Cottonwood Creek watersheds, San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.375,\n 37.1667\n ],\n [\n -119.833333,\n 37.1667\n ],\n [\n -119.833333,\n 36.75\n ],\n [\n -119.375,\n 36.75\n ],\n [\n -119.375,\n 37.1667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
In recent years, coral populations in the western Atlantic have undergone widespread declines from climate change, anthropogenic stressors, and infectious disease outbreaks. The pillar coral, Dendrogyra cylindrus, has been one of the most affected species, prompting its listing as threatened under the United States Endangered Species Act in 2014 and critically endangered under the IUCN Red List in 2022. However, due to its natural rarity, it is particularly difficult to study using conventional long-term monitoring studies or less common paleontological investigations. Here, we document for the first time, the multi-century persistence of D. cylindrus on high-latitude nearshore reefs off southeast Florida during the late Holocene. Using high-precision uranium–thorium (U-Th) dating, we constrain the ages of well-preserved subfossil D. cylindrus colonies recovered from newly described coral death assemblages. We also describe specific morphological characteristics and taphonomic indicators reflecting their unique depositional environment. Our findings demonstrate long-term persistence of D. cylindrus in southeast Florida, despite geographical isolation and historical rarity in the region.
Conservation and management of biological systems involves decision-making over time, with a generic goal of sustaining systems and their capacity to function in the future. We address four persistent and difficult conservation challenges: (1) prediction of future consequences of management, (2) uncertainty about the system's structure, (3) inability to observe ecological systems fully, and (4) nonstationary system dynamics. We describe these challenges in terms of dynamic systems subject to different sources of uncertainty, and we present a basic Markovian framework that can encompass approaches to all four challenges. Finding optimal conservation strategies for each challenge requires issue-specific structural features, including adaptations of state transition models, uncertainty metrics, valuation of accumulated returns, and solution methods. Strategy valuation exhibits not only some remarkable similarities among approaches but also some important operational differences. Technical linkages among the models highlight synergies in solution approaches, as well as possibilities for combining them in particular conservation problems. As methodology and computing software advance, such an integrated conservation framework offers the potential to improve conservation outcomes with strategies to allocate management resources efficiently and avoid negative consequences.
Diamondback terrapins (Malaclemys terrapin) are sexually dimorphic generalist turtles that inhabit salt marshes and estuaries along the Atlantic and Gulf coasts of the United States. On October 29th, 2012, Hurricane Sandy made landfall in New Jersey, USA, directly impacting terrapin populations inhabiting central and southern Barnegat Bay. To examine potential food web mediated impacts to the terrapin population and their foraging dynamics we examined carbon and nitrogen stable isotope values collected from terrapin tissues (2011, 2015, 2019) and resource taxa (2015, 2019) within Barnegat Bay. Isotopic analysis revealed that mature females had lower carbon and higher nitrogen values than immature females and males with almost no isotopic niche overlap, whereas males and immature females had statistically similar values with overlapping niches. Terrapins and resources collected from island habitats contained higher carbon and nitrogen values than those from mainland habitats, with little overlap in niche between habitats. There were no significant temporal variations detected in either carbon or nitrogen values from terrapins between years, or within each habitat pre- and post-Hurricane Sandy. These findings suggest long-term terrapin foraging dynamics have remained relatively stable, signifying resilience to disturbance events within the study site.
Over the past two decades, the U.S. Geological Survey (USGS) and other agencies have pioneered the use of active acoustic sensors to monitor suspended-sediment concentrations and particle sizes in rivers and streams at the subdaily time scale. The LISST-ABS submersible acoustic backscatter sediment sensor (or “ABS sensor”) was developed by Sequoia Scientific, Inc., as an alternative to turbidity sensors for monitoring suspended-sediment concentrations in surface waters. The ABS sensor is different than traditional active acoustic instruments because it is small, lower in cost, lightweight, and requires less power; and the sampling volume is within the first 15 centimeters of the transducer face. Initial testing by the USGS indicated the ABS sensor had utility as a novel, cost-effective, off-the-shelf tool for monitoring suspended-sediment concentration in surface waters, and its use within the agency has increased in since its introduction around 2016. However, initial testing did not account for the potential of transducer calibration drift over longer deployments.
As part of its mission to unify and standardize research and development activities of Federal agencies involved in fluvial sediment studies, the Federal Interagency Sedimentation Project partnered with the USGS Wyoming-Montana and New Mexico Water Science Centers to examine the potential for use of standard, low-tech laboratory equipment to perform calibration checks on ABS sensors on long-term deployments. The experiments were intended to provide USGS scientists and the public with interim guidance to assist in operating and maintaining the ABS sensor.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231039","collaboration":"Prepared in cooperation with the Federal Interagency Sedimentation Project","usgsCitation":"Alexander, J.S., O’Connell, J.P., and Brown, J.E., 2023, Interim guidance for calibration checks on a submersible acoustic backscatter sediment sensor (ver. 1.1, November 2023): U.S. Geological Survey Open-File Report 2023–1039, 23 p., https://doi.org/10.3133/ofr20231039.","productDescription":"Report: v, 23 p.; Data Release; 2 Datasets","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-147547","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":422961,"rank":8,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2023/1039/versionHist.txt","text":"Version History","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2023-1039 Version History"},{"id":416561,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://waterdata.usgs.gov/nwis/inventory/?site_no=09363500&agency_cd=USGS&","text":"USGS National Water Information System database","linkHelpText":"—USGS 09363500 Animas River near Cedar Hill, NM, in USGS water data for the Nation"},{"id":416559,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9M41G3W","text":"USGS data release","linkHelpText":"Data from lab experiments to support interim guidance for performing calibration checks on the Sequoia Scientific LISST-ABS acoustic backscatter sensor"},{"id":416557,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1039/ofr20231039.XML","size":"149 kB","description":"OFR 2023-1039 XML"},{"id":416555,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1039/coverthb2.jpg"},{"id":422960,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1039/ofr20231039.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023-1039"},{"id":416558,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1039/images"},{"id":416560,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"edition":"Version 1.0: May 1, 2023; Version 1.1: November 27, 2023","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601