Majuro Atoll in the central Pacific has high coastal vulnerability due to low-lying islands, rising sea level, high wave events, eroding shorelines, a dense population center, and limited freshwater resources. Land elevation is the primary geophysical variable that determines exposure to inundation in coastal settings. Accordingly, coastal elevation data (with accuracy information) are critical for assessments of inundation exposure. Previous research has demonstrated the importance of using high-accuracy elevation data and rigorously accounting for uncertainty in inundation assessments. A quantitative analysis of inundation exposure was conducted for Majuro Atoll, including accounting for the cumulative vertical uncertainty from the input digital elevation model (DEM) and datum transformation. The project employed a recently produced and validated DEM derived from structure-from-motion processing of very-high-resolution aerial imagery. Areas subject to marine inundation (direct hydrologic connection to the ocean) and low-lying lands (disconnected hydrologically from the ocean) were mapped and characterized for three inundation levels using deterministic and probabilistic methods. At the highest water level modeled (3.75 ft, or 1.143 m), more than 34% of the atoll study area is likely to be exposed to inundation (68% chance or greater), while more than 20% of the atoll is extremely likely to be exposed (95% chance or greater). The study demonstrates the substantial value of a high-accuracy DEM for assessing inundation exposure of low-relief islands and the enhanced information from accounting for vertical uncertainty.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12010154","usgsCitation":"Gesch, D.B., Palaseanu-Lovejoy, M., Danielson, J.J., Fletcher, C., Kottermair, M., Barbee, M., and Jalandoni, A., 2020, Inundation exposure assessment for Majuro Atoll, Republic of the Marshall Islands using a high-accuracy digital elevation model: Remote Sensing, v. 12, no. 1, Article: 154, 20 p.; Data Release, https://doi.org/10.3390/rs12010154.","productDescription":"Article: 154, 20 p.; Data Release","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":458218,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12010154","text":"Publisher Index Page"},{"id":372951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373335,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5ba9511ee4b08583a5ca09fe","text":"USGS data release","description":"USGS data release","linkHelpText":"Inundation exposure assessment for Majuro Atoll, Republic of the Marshall Islands"}],"country":"Republic of the Marshall Islands","otherGeospatial":"Majuro Atoll","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 170.98777770996094,\n 6.976183516197836\n ],\n [\n 171.43203735351562,\n 6.976183516197836\n ],\n [\n 171.43203735351562,\n 7.198319490613516\n ],\n [\n 170.98777770996094,\n 7.198319490613516\n ],\n [\n 170.98777770996094,\n 6.976183516197836\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Gesch, Dean B. 0000-0002-8992-4933 gesch@usgs.gov","orcid":"https://orcid.org/0000-0002-8992-4933","contributorId":2956,"corporation":false,"usgs":true,"family":"Gesch","given":"Dean","email":"gesch@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":784036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":784037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Danielson, Jeffrey J. 0000-0003-0907-034X daniels@usgs.gov","orcid":"https://orcid.org/0000-0003-0907-034X","contributorId":3996,"corporation":false,"usgs":true,"family":"Danielson","given":"Jeffrey","email":"daniels@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":784038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fletcher, Charles","contributorId":192304,"corporation":false,"usgs":false,"family":"Fletcher","given":"Charles","affiliations":[],"preferred":false,"id":784039,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kottermair, Maria","contributorId":119958,"corporation":false,"usgs":true,"family":"Kottermair","given":"Maria","email":"","affiliations":[],"preferred":false,"id":784040,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barbee, Matthew 0000-0002-8929-7255","orcid":"https://orcid.org/0000-0002-8929-7255","contributorId":196651,"corporation":false,"usgs":false,"family":"Barbee","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":784041,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jalandoni, Andrea 0000-0002-4821-7183","orcid":"https://orcid.org/0000-0002-4821-7183","contributorId":196653,"corporation":false,"usgs":false,"family":"Jalandoni","given":"Andrea","email":"","affiliations":[],"preferred":false,"id":784042,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70264996,"text":"70264996 - 2020 - A model for the growth and development of wave-dominated deltas fed by small mountainous rivers: Insights from the Elwha River delta, Washington","interactions":[],"lastModifiedDate":"2025-03-27T15:25:17.624368","indexId":"70264996","displayToPublicDate":"2020-01-03T10:20:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3369,"text":"Sedimentology","active":true,"publicationSubtype":{"id":10}},"title":"A model for the growth and development of wave-dominated deltas fed by small mountainous rivers: Insights from the Elwha River delta, Washington","docAbstract":"Observations from ground-penetrating radar, sediment cores, elevation surveys and aerial imagery are used to understand the development of the Elwha River delta in north-western Washington, USA, which prograded as a result of two dam removals in late 2011. Swash-bar, foreshore and swale depositional elements are recognized within ground-penetrating radar profiles and sediment cores. A model for the growth and development of small mountainous river wave-dominated deltas is proposed based on observation of both the fluvial and deltaic settings. If enough sediment is available in the fluvial system, mouth-bars form after higher than average river discharge events, creating a large platform seaward of the subaqueous delta plain. Swash-bars form concurrently or within a month of mouth-bar deposition as a result of wave action. Fair-weather waves drive swash-bar migration landward and in the direction of littoral drift. The signature of swash-bar welding to the shoreline is landward-dipping reflections, as a result of overwash processes and slipface migration. However, most swash-bars are eroded by the river mouth, as only 10 of the 37 swash-bars that formed between August 2011 and July 2016 survived within the Elwha River delta. The swash-bars that do survive either amalgamate onto the shoreline or an earlier deposited swash-bar, forming a single larger barrier at the delta front. In asymmetrical deltas, the signature of swash-bar welding is more likely to be preserved on the downdrift side of the delta, where formation is more likely and accommodation behind newer swash-bars preserves older deposits. On small mountainous river deltas, welded swash-bars may be more indicative of a large sediment pulse to the system, rather than large hydrological events.
","language":"English","publisher":"Wiley","doi":"10.1111/sed.12702","usgsCitation":"Zurbuchen, J., Simms, A., Warrick, J.A., Miller, I.M., and Ritchie, A., 2020, A model for the growth and development of wave-dominated deltas fed by small mountainous rivers: Insights from the Elwha River delta, Washington: Sedimentology, v. 67, no. 5, p. 2310-2331, https://doi.org/10.1111/sed.12702.","productDescription":"22 p.","startPage":"2310","endPage":"2331","ipdsId":"IP-091098","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":488702,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/sed.12702","text":"Publisher Index Page"},{"id":483949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Elwha River delta","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.53500604047304,\n 48.153518237078885\n ],\n [\n -123.57618620014911,\n 48.153518237078885\n ],\n [\n -123.57618620014911,\n 48.12519411609762\n ],\n [\n -123.53500604047304,\n 48.12519411609762\n ],\n [\n -123.53500604047304,\n 48.153518237078885\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"67","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Zurbuchen, Julie","contributorId":352837,"corporation":false,"usgs":false,"family":"Zurbuchen","given":"Julie","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":932190,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Simms, Alexander R.","contributorId":352838,"corporation":false,"usgs":false,"family":"Simms","given":"Alexander R.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":932191,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warrick, Jonathan A. 0000-0002-0205-3814 jwarrick@usgs.gov","orcid":"https://orcid.org/0000-0002-0205-3814","contributorId":167736,"corporation":false,"usgs":true,"family":"Warrick","given":"Jonathan","email":"jwarrick@usgs.gov","middleInitial":"A.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932192,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Ian M. 0000-0002-3289-6337","orcid":"https://orcid.org/0000-0002-3289-6337","contributorId":41951,"corporation":false,"usgs":false,"family":"Miller","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":932193,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ritchie, Andrew C. 0000-0001-5826-9983","orcid":"https://orcid.org/0000-0001-5826-9983","contributorId":333630,"corporation":false,"usgs":true,"family":"Ritchie","given":"Andrew C.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":932194,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70248462,"text":"70248462 - 2020 - Geologic map of the Patrick quadrangle, Chesterfield County, South Carolina","interactions":[],"lastModifiedDate":"2023-09-15T13:25:41.276194","indexId":"70248462","displayToPublicDate":"2020-01-01T08:54:20","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":16711,"text":"Geologic Quadrangle Map","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"GQM-57","title":"Geologic map of the Patrick quadrangle, Chesterfield County, South Carolina","docAbstract":"The Patrick 7.5 minute quadrangle, located in Chesterfield County, South Carolina, lies entirely within the upper Atlantic Coastal Plain province. Directly to the southeast in the Dovesville quadrangle, the Pliocene Orangeburg Scarp marks the western edge of marine terraces that characterize the upper limit of the middle Atlantic Coastal Plain. The geologic mapping for this quadrangle was done from 2013-2015 by Bradley A. Fitzwater at Old Dominion University as part of a Master’s thesis supervised by G. Richard Whittecar. Christopher S. Swezey (U.S. Geological Survey) and Fitzwater collaborated in the geologic mapping of both the Patrick quadrangle and the adjacent Middendorf quadrangle (Swezey et al., 2021). The geologic mapping was conducted using a lidar base from 2013, whereas this published product shows the geologic data on a USGS topographic map base from 1968. As a result of differences in resolution, the published map may display a few minor discrepancies with respect to alignment of geologic data with topographic and hydrologic features.
","language":"English","publisher":"South Carolina Geological Survey","usgsCitation":"Fitzwater, B.A., Whittecar, G., and Swezey, C.S., 2020, Geologic map of the Patrick quadrangle, Chesterfield County, South Carolina: Geologic Quadrangle Map GQM-57, 1 Plate: 42.00 x 32.00 inches.","productDescription":"1 Plate: 42.00 x 32.00 inches","ipdsId":"IP-082620","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":420827,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_115090.htm"},{"id":420784,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.dnr.sc.gov/geology/publications.html"},{"id":420789,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"scale":"24000","country":"United States","state":"South Carolina","county":"Chesterfield County","otherGeospatial":"Patrick quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.125,\n 34.625\n ],\n [\n -80.125,\n 34.5\n ],\n [\n -80,\n 34.5\n ],\n [\n -80,\n 34.625\n ],\n [\n -80.125,\n 34.625\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fitzwater, Bradley A.","contributorId":177211,"corporation":false,"usgs":false,"family":"Fitzwater","given":"Bradley","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Whittecar, G. Richard","contributorId":313541,"corporation":false,"usgs":false,"family":"Whittecar","given":"G. Richard","affiliations":[{"id":36518,"text":"Old Dominion University","active":true,"usgs":false}],"preferred":false,"id":883007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swezey, Christopher S. 0000-0003-4019-9264 cswezey@usgs.gov","orcid":"https://orcid.org/0000-0003-4019-9264","contributorId":173033,"corporation":false,"usgs":true,"family":"Swezey","given":"Christopher","email":"cswezey@usgs.gov","middleInitial":"S.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":883008,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222101,"text":"70222101 - 2020 - Patterns and drivers of atmospheric river precipitation and hydrologic impacts across the western United States","interactions":[],"lastModifiedDate":"2021-07-21T11:56:46.001135","indexId":"70222101","displayToPublicDate":"2020-01-01T07:07:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2344,"text":"Journal of Hydrometeorology","active":true,"publicationSubtype":{"id":10}},"title":"Patterns and drivers of atmospheric river precipitation and hydrologic impacts across the western United States","docAbstract":"Atmospheric rivers (ARs) significantly influence precipitation and hydrologic variability in many areas of the world, including the western United States. As ARs are increasingly recognized by the research community and the public, there is a need to more precisely quantify and communicate their hydrologic impacts, which can vary from hazardous to beneficial depending on location and on the atmospheric and land surface conditions prior to and during the AR. This study leverages 33 years of atmospheric and hydrologic data for the western United States to 1) identify how water vapor amount, wind direction and speed, temperature, and antecedent soil moisture conditions influence precipitation and hydrologic responses (runoff, recharge, and snowpack) using quantile regression and 2) identify differences in hydrologic response types and magnitudes across the study region. Results indicate that water vapor amount serves as a primary control on precipitation amounts. Holding water vapor constant, precipitation amounts vary with wind direction, depending on location, and are consistently greater at colder temperatures. Runoff efficiencies further covary with temperature and antecedent soil moisture, with precipitation falling as snow and greater available water storage in the soil column mitigating flood impacts of large AR events. This study identifies the coastal and maritime mountain ranges as areas with the greatest potential for hazardous flooding and snowfall impacts. This spatially explicit information can lead to better understanding of the conditions under which ARs of different precipitation amounts are likely to be hazardous at a given location.
","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JHM-D-19-0119.1","usgsCitation":"Albano, C.M., Dettinger, M.D., and Harpold, A., 2020, Patterns and drivers of atmospheric river precipitation and hydrologic impacts across the western United States: Journal of Hydrometeorology, v. 21, p. 143-159, https://doi.org/10.1175/JHM-D-19-0119.1.","productDescription":"17 p.","startPage":"143","endPage":"159","ipdsId":"IP-108504","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":458275,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/jhm-d-19-0119.1","text":"Publisher Index Page"},{"id":387290,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -127.3095703125,\n 31.541089879585808\n ],\n [\n -108.9404296875,\n 31.541089879585808\n ],\n [\n -108.9404296875,\n 49.26780455063753\n ],\n [\n -127.3095703125,\n 49.26780455063753\n ],\n [\n -127.3095703125,\n 31.541089879585808\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Albano, Christine M.","contributorId":169455,"corporation":false,"usgs":false,"family":"Albano","given":"Christine","email":"","middleInitial":"M.","affiliations":[{"id":12711,"text":"UC Davis","active":true,"usgs":false}],"preferred":false,"id":819519,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dettinger, Michael D. 0000-0002-7509-7332 mddettin@usgs.gov","orcid":"https://orcid.org/0000-0002-7509-7332","contributorId":149896,"corporation":false,"usgs":true,"family":"Dettinger","given":"Michael","email":"mddettin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":819520,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harpold, Adrian","contributorId":184147,"corporation":false,"usgs":false,"family":"Harpold","given":"Adrian","affiliations":[],"preferred":false,"id":819521,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208100,"text":"70208100 - 2020 - Thresholds for post-wildfire debris flows: Insights from the Pinal Fire, Arizona, USA","interactions":[],"lastModifiedDate":"2020-06-04T16:48:14.988077","indexId":"70208100","displayToPublicDate":"2019-12-27T07:11:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Thresholds for post-wildfire debris flows: Insights from the Pinal Fire, Arizona, USA","docAbstract":"Wildfire significantly alters the hydrologic properties of a burned area, leading to increases in overland flow, erosion, and the potential for runoff-generated debris flows. The initiation of debris flows in recently burned areas is well-characterized by rainfall intensity-duration (ID) thresholds. However, there is currently a paucity of data quantifying the rainfall intensities required to trigger post-wildfire debris flows, which limits our understanding of how and why rainfall ID thresholds vary in different climatic and geologic settings. In this study, we monitored debris-flow activity following the Pinal Fire in central Arizona, which differs from both a climatic and hydrogeomorphic perspective from other regions in the western U.S. where ID thresholds for post-wildfire debris flows are well-established, namely the Transverse Ranges of southern CA. Since the peak rainfall intensity within a rainstorm may exceed the rainfall intensity required to trigger a debris flow, the development of robust rainfall ID thresholds requires knowledge of the timing of debris flows within rainstorms. Existing post-wildfire debris-flow studies in Arizona only constrain the peak rainfall intensity within debris-flow-producing storms, which may far exceed the intensity that actually triggered the observed debris flow. In this study, we used pressure transducers within 5 burned drainage basins to constrain the timing of debris flows within rainstorms. Rainfall ID thresholds derived here from triggering rainfall intensities are, on average, 22 mm/h lower than ID thresholds derived under the assumption that the triggering intensity is equal to the maximum rainfall intensity recorded during a rainstorm. We then use a hydrologic model to demonstrate that the magnitude of the 15-minute rainfall ID threshold at the Pinal Fire site is associated with the rainfall intensity required to exceed a recently proposed dimensionless discharge threshold for debris-flow initiation. Model results further suggest that previously observed differences in regional ID thresholds between Arizona and the San Gabriel Mountains of southern CA may be attributed, in large part, to differences in the hydraulic properties of burned soils.","language":"English","publisher":"Wiley","doi":"10.1002/esp.4805","usgsCitation":"Raymond, C.A., McGuire, L.A., Youberg, A.M., Staley, D.M., and Kean, J.W., 2020, Thresholds for post-wildfire debris flows: Insights from the Pinal Fire, Arizona, USA: Earth Surface Processes and Landforms, v. 45, no. 6, p. 1349-1360, https://doi.org/10.1002/esp.4805.","productDescription":"12 p.","startPage":"1349","endPage":"1360","ipdsId":"IP-112967","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":371633,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.994873046875,\n 33.25936011503665\n ],\n [\n -110.60348510742188,\n 33.25936011503665\n ],\n [\n -110.60348510742188,\n 33.543683878655926\n ],\n [\n -110.994873046875,\n 33.543683878655926\n ],\n [\n -110.994873046875,\n 33.25936011503665\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"45","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Raymond, Carissa A","contributorId":221837,"corporation":false,"usgs":false,"family":"Raymond","given":"Carissa","email":"","middleInitial":"A","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":780463,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Luke A. 0000-0001-8178-7922 lmcguire@usgs.gov","orcid":"https://orcid.org/0000-0001-8178-7922","contributorId":203420,"corporation":false,"usgs":false,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","middleInitial":"A.","affiliations":[{"id":7042,"text":"University of Arizona","active":true,"usgs":false}],"preferred":false,"id":780464,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Youberg, Ann M. 0000-0002-2005-3674","orcid":"https://orcid.org/0000-0002-2005-3674","contributorId":172609,"corporation":false,"usgs":false,"family":"Youberg","given":"Ann","email":"","middleInitial":"M.","affiliations":[{"id":6672,"text":"former: USGS Southwest Biological Science Center, Colorado Plateau Research Station, Flagstaff, AZ. Current address: TN-SCORE, Univ of Tennessee, Knoxville, TN, e-mail: jennen@gmail.com","active":true,"usgs":false}],"preferred":true,"id":780465,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":780466,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":780462,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209758,"text":"70209758 - 2020 - Effect of an environmental flow on vegetation growth and health using ground and remote sensing metrics","interactions":[],"lastModifiedDate":"2020-04-28T14:24:02.273893","indexId":"70209758","displayToPublicDate":"2019-12-24T08:13:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Effect of an environmental flow on vegetation growth and health using ground and remote sensing metrics","docAbstract":"Understanding the effectiveness of environmental flow deliveries along rivers requires monitoring vegetation. Monitoring data are often collected at multiple spatial scales. For riparian vegetation, optical remote sensing methods can estimate growth responses at the riparian corridor scale, and field‐based measures can quantify species composition; however, the extent to which these different measures are duplicative or complementary is important to understand when planning monitoring programmes with limited resources. In this study, we analysed riparian vegetation growth in the delta of the Colorado River in response to an experimental pulse flow. Our goal was to compare ground‐based measurements of vegetation structure and composition with satellite‐based Landsat radiometric variables, such as the normalized difference vegetation index (NDVI). We made this comparison in 21 transects following the delivery of 131.8 million cubic meters (mcm) of water in the stream channel during the spring of 2014 as a pulse flow and 38.4 mcm as base flows. Vegetation cover increased 14% and NDVI increased 0.02 (15%) by October 2015, and both variables returned to pre‐pulse flow values in October 2016. Observed changes in vegetation structure and composition did not persist after the second year. The highest increase in vegetation cover in October 2014 and October 2015 resulted from species that could respond rapidly to additional water such as reeds (Arundo donax and Phragmites australis), cattail (Typha domingensis), and herbaceous plants. Dominant shrubs, saltcedar (Tamarix spp.) and arrowweed (Pluchea sericea), both indicative of nonrestored habitats showed variable increases in cover, and native trees (Salicaceae family) presented low increases (1%). The strong NDVI–vegetation cover relationship indicates that NDVI is appropriate to detect changes at the riparian corridor scale but needs to be complemented with ground data to determine the contributions by different species to the observed trends.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13689","collaboration":"","usgsCitation":"Gomez-Sapiens, M.M., Jarchow, C., Flessa, K.W., Shafroth, P.B., Glenn, E., and Nagler, P.L., 2020, Effect of an environmental flow on vegetation growth and health using ground and remote sensing metrics: Hydrological Processes, v. 34, no. 8, p. 1682-1696, https://doi.org/10.1002/hyp.13689.","productDescription":"15 p.","startPage":"1682","endPage":"1696","ipdsId":"IP-109952","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":488909,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/659868","text":"External Repository"},{"id":374314,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.3070068359375,\n 31.5691754490709\n ],\n [\n -114.70275878906249,\n 31.5691754490709\n ],\n [\n -114.70275878906249,\n 32.708733368521585\n ],\n [\n -115.3070068359375,\n 32.708733368521585\n ],\n [\n -115.3070068359375,\n 31.5691754490709\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-02-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Gomez-Sapiens, Martha M.","contributorId":58172,"corporation":false,"usgs":true,"family":"Gomez-Sapiens","given":"Martha","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":787897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":787898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flessa, Karl W.","contributorId":175308,"corporation":false,"usgs":false,"family":"Flessa","given":"Karl","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":787899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X shafrothp@usgs.gov","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":2000,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick","email":"shafrothp@usgs.gov","middleInitial":"B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":787900,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Glenn, Edward P.","contributorId":56542,"corporation":false,"usgs":false,"family":"Glenn","given":"Edward P.","affiliations":[{"id":13060,"text":"Department of Soil, Water and Environmental Science, University of Arizona","active":true,"usgs":false}],"preferred":false,"id":787901,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":787902,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209448,"text":"70209448 - 2020 - Environmental tracer evidence for connection between shallow and bedrock aquifers and high intrinsic susceptibility to contamination of the conterminous U.S. glacial aquifer","interactions":[],"lastModifiedDate":"2020-05-04T18:29:03.706787","indexId":"70209448","displayToPublicDate":"2019-12-23T07:20:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Environmental tracer evidence for connection between shallow and bedrock aquifers and high intrinsic susceptibility to contamination of the conterminous U.S. glacial aquifer","docAbstract":"Covering a large portion of the northern conterminous United States (1.87 x 106 km2), the glacial aquifer serves as the primary water supply for 39 million public and domestic water users. Mean groundwater age, groundwater age distribution, and susceptibility to land surface contamination, using a new metric (Susceptibility Index; SI) based on the full age distribution and less prone to bias than estimated mean age, is reported for 168 public and domestic wells across the aquifer. Comparison of groundwater age metrics between well networks of varying spatial scale suggest an extensive sample network of equally spaced, long screened interval wells can be used to characterize aquifer wide groundwater age. Estimated mean age ranges from 1 to 50,000 years and, according to the composite age distribution, approximately 63 percent of all sampled water recharged after 1950 (i.e., modern) and 18 percent of the sampled water was recharged greater than 10,000 years ago. The later finding strongly suggests a connection between the glacial aquifer and underlying bedrock aquifers. Statistical analysis of glacial aquifer hydrogeology and age metrics show groundwater ages are young (less than few 100 years) and more susceptible to land surface contamination (larger SI) in unconfined and shallow portions of the aquifer. Old groundwater (greater than 1000 years) is more often associated with thicker sequences of fine grain sediments and/or shallow bedrock. Calculated SI is shown to be more strongly related to the number of land surface contaminants detected than mean age or fraction modern. Statistical analysis of SI and hydrogeology indicates SI is largely dictated by well depth and confinement. This study demonstrates how sample network design can be used to characterize groundwater age of large aquifers with a limited number of samples and how interpretation of environmental tracers can be used to improve conceptual models of groundwater aquifers and identify groundwater susceptible to contamination.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.124505","collaboration":"","usgsCitation":"Solder, J.E., Jurgens, B., Stackelberg, P.E., and Shope, C., 2020, Environmental tracer evidence for connection between shallow and bedrock aquifers and high intrinsic susceptibility to contamination of the conterminous U.S. glacial aquifer: Journal of Hydrology, v. 583, 124505, 12 p., https://doi.org/10.1016/j.jhydrol.2019.124505.","productDescription":"124505, 12 p.","ipdsId":"IP-090099","costCenters":[{"id":610,"text":"Utah Water Science 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0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":786518,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shope, Christopher L. 0000-0003-4209-049X","orcid":"https://orcid.org/0000-0003-4209-049X","contributorId":223873,"corporation":false,"usgs":false,"family":"Shope","given":"Christopher L.","affiliations":[{"id":40783,"text":"State of Utah Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":786519,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215153,"text":"70215153 - 2020 - A hydrologic landscapes perspective on groundwater connectivity of depressional wetlands","interactions":[],"lastModifiedDate":"2020-10-08T14:52:59.912851","indexId":"70215153","displayToPublicDate":"2019-12-21T09:46:32","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"A hydrologic landscapes perspective on groundwater connectivity of depressional wetlands","docAbstract":"Simplification of communities is a common consequence of anthropogenic modification. However, the prevalence and mechanisms of biotic homogenization among wetland systems require further examination. Biota of wetlands in the North American Prairie Pothole Region are adapted to high spatial and temporal variability in ponded-water duration and salinity. Recent climate change, however, has resulted in decreased hydrologic variability. Land-use changes have exacerbated this loss of variability. We used aquatic-macroinvertebrate data from 16 prairie-pothole wetlands sampled between 1992 and 2015 to explore homogenization of wetland communities. Macroinvertebrate communities of small wetlands that continued to cycle between wet and dry phases experienced greater turnover and supported unique taxa compared to larger wetlands that shifted towards less dynamic permanently ponded, lake-like regimes. Temporal turnover in beta-diversity was lowest in these permanently ponded wetlands. Additionally, wetlands that shifted to permanently ponded regimes also experienced a shift from palustrine to lacustrine communities. While increased pond permanence can increase species and overall beta-diversity in local areas previously lacking lake communities, homogenization of wetland communities at a larger, landscape scale can result in an overall loss of biodiversity as the diverse communities of many wetland systems become increasingly similar to those of lakes.
","language":"English","publisher":"Springer International Publishing","doi":"10.1007/s10750-019-04154-4","usgsCitation":"McLean, K., Mushet, D.M., Sweetman, J.N., Anteau, M.J., and Wiltermuth, M.T., 2020, Invertebrate communities of Prairie-Pothole wetlands in the age of the aquatic Homogenocene: Hydrobiologia, v. 847, p. 3773-3793, https://doi.org/10.1007/s10750-019-04154-4.","productDescription":"21 p.","startPage":"3773","endPage":"3793","ipdsId":"IP-111199","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":370671,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","county":"Stutsman County","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.77999114990234,\n 47.820762392755846\n ],\n [\n -100.63407897949219,\n 47.820762392755846\n ],\n [\n -100.63407897949219,\n 47.939116930322\n ],\n [\n -100.77999114990234,\n 47.939116930322\n ],\n [\n -100.77999114990234,\n 47.820762392755846\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"847","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778407,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778408,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sweetman, Jon N. 0000-0002-9849-7355","orcid":"https://orcid.org/0000-0002-9849-7355","contributorId":221489,"corporation":false,"usgs":false,"family":"Sweetman","given":"Jon","email":"","middleInitial":"N.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":778409,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anteau, Michael J. 0000-0002-5173-5870 manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":778410,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":778411,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209440,"text":"70209440 - 2020 - Time scales of arsenic variability and the role of high-frequency monitoring at three water-supply wells in New Hampshire, USA","interactions":[],"lastModifiedDate":"2020-05-05T12:11:42.664539","indexId":"70209440","displayToPublicDate":"2019-12-14T19:51:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Time scales of arsenic variability and the role of high-frequency monitoring at three water-supply wells in New Hampshire, USA","docAbstract":"Groundwater geochemistry, redox process classification, high-frequency physicochemical and hydrologic measurements, and climate data were analyzed to identify controls on arsenic (As) concentration changes. Groundwater was monitored in two public-supply wells (one glacial aquifer and one bedrock aquifer), and one bedrock-aquifer domestic well in New Hampshire, USA, from 2014 to 2018 to identify time scales of and controls on As concentration changes. Concentrations of As and other geochemical constituents were measured bimonthly. Specific conductance (SC), pH, dissolved oxygen, and pumping rate/water level were measured at high frequency (every 5 to 15 min). Median (and 95% confidence interval) As concentrations at the three wells were 4.1 (3.7–4.6), 18.9 (17.2–23.6), and 37.5 (30.4–42.9) μg/L. Arsenic variability in each of the three wells, in relative standard deviation, ranged from 9 to 12%. Median quarterly As concentrations were highest in all wells in the spring. The bedrock-aquifer public-supply well As concentration increased over the period of study while pumping rate decreased. In the public-supply wells, As variability was correlated with SC and pH, and As species were related to SC, pH, pumping, precipitation, and changes in redox process. Specific conductance also had a seasonal pattern in the two public-supply wells and was correlated with Na and Cl. Excess Na in water samples suggests possible ion exchange with dissolved Ca, creating more capacity to dissolve CaCO3 from calcareous rocks, which can increase pH and in turn, As concentrations in wells. High-frequency monitoring data are cost effective to collect, which could be advantageous in other parts of the United States and in the many parts of the world where glacial aquifers are in direct contact with other water supply aquifers or where water from different aquifers have potential to mix.
Remediation of mercury (Hg) contaminated sites has long relied on traditional approaches, such as removal and containment/capping. Here we review contemporary practices in the assessment and remediation of industrial-scale Hg contaminated sites and discuss recent advances. Significant improvements have been made in site assessment, including the use of XRF to rapidly identify the spatial extent of contamination, Hg stable isotope fractionation to identify sources and transformation processes, and solid-phase characterization (XAFS) to evaluate Hg forms. The understanding of Hg bioavailability for methylation has been improved by methods such as sequential chemical extractions and porewater measurements, including the use of diffuse gradient in thin-film (DGT) samplers. These approaches have shown varying success in identifying bioavailable Hg fractions and further study and field applications are needed. The downstream accumulation of methylmercury (MeHg) in biota is a concern at many contaminated sites. Identifying the variables limiting/controlling MeHg production—such as bioavailable inorganic Hg, organic carbon, and/or terminal electron acceptors (e.g. sulfate, iron) is critical. Mercury can be released from contaminated sites to the air and water, both of which are influenced by meteorological and hydrological conditions. Mercury mobilized from contaminated sites is predominantly bound to particles, highly correlated with total sediment solids (TSS), and elevated during stormflow. Remediation techniques to address Hg contamination can include the removal or containment of Hg contaminated materials, the application of amendments to reduce mobility and bioavailability, landscape/waterbody manipulations to reduce MeHg production, and food web manipulations through stocking or extirpation to reduce MeHg accumulated in desired species. These approaches often rely on knowledge of the Hg forms/speciation at the site, and utilize physical, chemical, thermal and biological methods to achieve remediation goals. Overall, the complexity of Hg cycling allows many different opportunities to reduce/mitigate impacts, which creates flexibility in determining suitable and logistically feasible remedies.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2019.136031","usgsCitation":"Eckley, C.S., Gilmour, C.C., Janssen, S., Luxton, T., Randall, P.M., Whalin, L., and Austin, C., 2020, The assessment and remediation of mercury contaminated sites: A review of current approaches: Science of the Total Environment, v. 707, 136031, 19 p., https://doi.org/10.1016/j.scitotenv.2019.136031.","productDescription":"136031, 19 p.","ipdsId":"IP-111241","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":458364,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6980986","text":"External Repository"},{"id":370681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"707","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eckley, Chris S.","contributorId":167256,"corporation":false,"usgs":false,"family":"Eckley","given":"Chris","email":"","middleInitial":"S.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":778497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilmour, Cynthia C","contributorId":221508,"corporation":false,"usgs":false,"family":"Gilmour","given":"Cynthia","email":"","middleInitial":"C","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":778498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":778496,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Luxton, Todd P","contributorId":221509,"corporation":false,"usgs":false,"family":"Luxton","given":"Todd P","affiliations":[{"id":40396,"text":"US Environmental Protection Agency, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":778499,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Randall, Paul M","contributorId":221510,"corporation":false,"usgs":false,"family":"Randall","given":"Paul","email":"","middleInitial":"M","affiliations":[{"id":40396,"text":"US Environmental Protection Agency, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":778500,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whalin, Lindsay","contributorId":221511,"corporation":false,"usgs":false,"family":"Whalin","given":"Lindsay","email":"","affiliations":[{"id":40397,"text":"San Francisco Bay Water Board","active":true,"usgs":false}],"preferred":false,"id":778501,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Austin, Carrie","contributorId":221512,"corporation":false,"usgs":false,"family":"Austin","given":"Carrie","email":"","affiliations":[{"id":40397,"text":"San Francisco Bay Water Board","active":true,"usgs":false}],"preferred":false,"id":778502,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209441,"text":"70209441 - 2020 - Chronic and episodic acidification of streams along the Appalachian Trail corridor, eastern United States","interactions":[],"lastModifiedDate":"2020-05-04T18:25:19.107285","indexId":"70209441","displayToPublicDate":"2019-12-12T07:59:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Chronic and episodic acidification of streams along the Appalachian Trail corridor, eastern United States","docAbstract":"Acidic atmospheric deposition has adversely affected aquatic ecosystems globally. As emissions and deposition of sulfur (S) and nitrogen (N) have declined in recent decades across North America and Europe, ecosystem recovery is evident in many surface waters. However, persistent chronic and episodic acidification remain important concerns in vulnerable regions. We evaluated acidification in 269 headwater streams during 2010–2012 along the Appalachian Trail (AT) that transits several ecoregions and is located downwind of high levels of S and N emission sources. Discharge was estimated by matching sampled streams to those of a nearby gaged stream and assuming equivalent daily mean flow percentiles. Charge balance acid‐neutralizing capacity (ANC) values were adjusted to the 15th (Q15) and 85th flow percentiles (Q85) by applying the ANC/discharge slope among sample pairs collected at each stream. A site‐based approach was applied to streams sampled twice or more and a second regression‐based approach to streams sampled once to estimate episodic acidification magnitudes as the ANC difference from Q15 to Q85. Streams with ANC <0 μeq/L doubled from 16% to 32% as discharge increased from Q15 to Q85 according to the site‐based approach. The proportion of streams with ANC <0 μeq/L at low flow and high flow decreased from north to south. Base cation dilution explained the greatest amount of episodic acidification among streams and variation in sulfate (SO42−) concentrations was a secondary explanatory variable. Episodic SO42− patterns varied geographically with dilution dominant in northern streams underlain by soils developed in glacial sediment and increased concentrations dominant in southern streams with older, highly weathered soils. Episodic acidification increased as low‐flow ANC increased, exceeding 90 μeq/L in 25% of streams. Episodic increases in ANC were the dominant pattern in streams with low‐flow ANC values <30 μeq/L. Chronic and episodic acidification remain an ecological concern among AT streams. The approach developed here could be applied to estimate the magnitude and extent of chronic and episodic acidification in other regions recovering from decreasing levels of atmospheric S and N deposition.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.13668","collaboration":"","usgsCitation":"Burns, D., McDonnell, T., Rice, K.C., Lawrence, G.B., and Sullivan, T., 2020, Chronic and episodic acidification of streams along the Appalachian Trail corridor, eastern United States: Hydrological Processes, v. 34, p. 1498-1513, https://doi.org/10.1002/hyp.13668.","productDescription":"16 p.","startPage":"1498","endPage":"1513","ipdsId":"IP-109972","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":458377,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.13668","text":"Publisher Index Page"},{"id":373837,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Connecticut, Georgia, Maine, Massachusetts, Maryland, New Hampshire, New Jersey, New York, North Carolina, Pennsylvania, Tennessee, Vermont, Virginia","otherGeospatial":"Appalachian Trail corridor","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.671875,\n 32.509761735919426\n ],\n [\n -82.08984375,\n 32.02670629333614\n ],\n [\n -79.62890625,\n 33.02708758002874\n ],\n [\n -76.9921875,\n 35.67514743608467\n ],\n [\n -76.5966796875,\n 37.61423141542417\n ],\n [\n -76.552734375,\n 38.89103282648846\n ],\n [\n -75.2783203125,\n 40.413496049701955\n ],\n [\n -71.7626953125,\n 42.52069952914966\n ],\n [\n -70.3564453125,\n 43.644025847699496\n ],\n [\n -69.521484375,\n 44.465151013519616\n ],\n [\n -68.15917968749999,\n 45.058001435398275\n ],\n [\n -68.02734375,\n 46.164614496897094\n ],\n [\n -68.291015625,\n 46.6795944656402\n ],\n [\n -69.345703125,\n 46.46813299215554\n ],\n [\n -70.5322265625,\n 45.213003555993964\n ],\n [\n -72.158203125,\n 44.653024159812\n ],\n [\n -74.8388671875,\n 43.389081939117496\n ],\n [\n -75.76171875,\n 42.00032514831621\n ],\n [\n -78.22265625,\n 40.68063802521456\n ],\n [\n -79.013671875,\n 39.87601941962116\n ],\n [\n -80.244140625,\n 38.37611542403604\n ],\n [\n -81.650390625,\n 35.28150065789119\n ],\n [\n -83.8037109375,\n 34.08906131584994\n ],\n [\n -84.111328125,\n 33.50475906922609\n ],\n [\n -83.671875,\n 32.509761735919426\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","noUsgsAuthors":false,"publicationDate":"2020-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Burns, Douglas A. 0000-0001-6516-2869","orcid":"https://orcid.org/0000-0001-6516-2869","contributorId":202943,"corporation":false,"usgs":true,"family":"Burns","given":"Douglas A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":786490,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McDonnell, Todd","contributorId":223867,"corporation":false,"usgs":false,"family":"McDonnell","given":"Todd","affiliations":[{"id":40780,"text":"E&S Environmental Chemistry","active":true,"usgs":false}],"preferred":false,"id":786491,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rice, Karen C. 0000-0002-9356-5443 kcrice@usgs.gov","orcid":"https://orcid.org/0000-0002-9356-5443","contributorId":178269,"corporation":false,"usgs":true,"family":"Rice","given":"Karen","email":"kcrice@usgs.gov","middleInitial":"C.","affiliations":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"preferred":true,"id":786492,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawrence, Gregory B. 0000-0002-8035-2350 glawrenc@usgs.gov","orcid":"https://orcid.org/0000-0002-8035-2350","contributorId":867,"corporation":false,"usgs":true,"family":"Lawrence","given":"Gregory","email":"glawrenc@usgs.gov","middleInitial":"B.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":786493,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sullivan, Timothy","contributorId":223868,"corporation":false,"usgs":false,"family":"Sullivan","given":"Timothy","affiliations":[{"id":40780,"text":"E&S Environmental Chemistry","active":true,"usgs":false}],"preferred":false,"id":786494,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208280,"text":"70208280 - 2020 - Potential changes to the biology and challenges to the management of invasive sea lamprey Petromyzon marinus in the Laurentian Great Lakes due to climate change","interactions":[],"lastModifiedDate":"2020-03-11T15:11:17","indexId":"70208280","displayToPublicDate":"2019-12-12T06:53:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Potential changes to the biology and challenges to the management of invasive sea lamprey Petromyzon marinus in the Laurentian Great Lakes due to climate change","title":"Potential changes to the biology and challenges to the management of invasive sea lamprey Petromyzon marinus in the Laurentian Great Lakes due to climate change","docAbstract":"Control programs are implemented to mitigate the damage caused by invasive species worldwide. In the highly invaded Great Lakes, the climate is expected to become warmer with more extreme weather and variable precipitation, resulting in shorter iced‐over periods and variable tributary flows as well as changes to pH and river hydrology and hydrogeomorphology. We review how climate change influences physiology, behavior, and demography of a damaging invasive species, sea lamprey (Petromyzon marinus), in the Great Lakes, and the consequences for sea lamprey control efforts. Sea lamprey control relies on surveys to monitor abundance of larval sea lamprey in Great Lakes tributaries. The abundance of parasitic, juvenile sea lampreys in the lakes is calculated by surveying wounding rates on lake trout (Salvelinus namaycush), and trap surveys are used to enumerate adult spawning runs. Chemical control using lampricides (i.e., lamprey pesticides) to target larval sea lamprey and barriers to prevent adult lamprey from reaching spawning grounds are the most important tools used for sea lamprey population control. We describe how climate change could affect larval survival in rivers, growth and maturation in lakes, phenology and the spawning migration as adults return to rivers, and the overall abundance and distribution of sea lamprey in the Great Lakes. Our review suggests that Great Lakes sea lamprey may benefit from climate change with longer growing seasons, more rapid growth, and greater access to spawning habitat, but uncertainties remain about the future availability and suitability of larval habitats. Consideration of the biology of invasive species and adaptation of the timing, intensity, and frequency of control efforts is critical to the management of biological invasions in a changing world, such as sea lamprey in the Great Lakes.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.14957","usgsCitation":"Lennox, R.J., Bravener, G.A., Lin, H., Madenjian, C.P., Muir, A.M., Remucal, C.K., Robinson, K., Rous, A.M., Siefkes, M.J., Wilkie, M.P., Zielinski, D.P., and Cooke, S.J., 2020, Potential changes to the biology and challenges to the management of invasive sea lamprey Petromyzon marinus in the Laurentian Great Lakes due to climate change: Global Change Biology, v. 26, no. 3, p. 1118-1137, https://doi.org/10.1111/gcb.14957.","productDescription":"20 p.","startPage":"1118","endPage":"1137","ipdsId":"IP-109166","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":458379,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/gcb.14957","text":"Publisher Index Page"},{"id":371897,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada ","otherGeospatial":"Great Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.07617187499999,\n 41.11246878918088\n ],\n [\n -75.8056640625,\n 41.11246878918088\n ],\n [\n -75.8056640625,\n 49.35375571830993\n ],\n [\n -93.07617187499999,\n 49.35375571830993\n ],\n [\n -93.07617187499999,\n 41.11246878918088\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"3","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Lennox, Robert J.","contributorId":198273,"corporation":false,"usgs":false,"family":"Lennox","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":781233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bravener, Gale A.","contributorId":222107,"corporation":false,"usgs":false,"family":"Bravener","given":"Gale","email":"","middleInitial":"A.","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":781234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lin, Hsien-Yung","contributorId":222108,"corporation":false,"usgs":false,"family":"Lin","given":"Hsien-Yung","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":781235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science 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University","active":true,"usgs":false}],"preferred":false,"id":781238,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rous, Andrew M.","contributorId":203583,"corporation":false,"usgs":false,"family":"Rous","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":36663,"text":"Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada","active":true,"usgs":false}],"preferred":false,"id":781239,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Siefkes, Michael J.","contributorId":222109,"corporation":false,"usgs":false,"family":"Siefkes","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":781240,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wilkie, Michael P.","contributorId":191045,"corporation":false,"usgs":false,"family":"Wilkie","given":"Michael","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":781241,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zielinski, Daniel P.","contributorId":211034,"corporation":false,"usgs":false,"family":"Zielinski","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":34820,"text":"Great Lakes Fisheries Commission, Ann Arbor, MI","active":true,"usgs":false}],"preferred":false,"id":781243,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Cooke, Steven J.","contributorId":214435,"corporation":false,"usgs":false,"family":"Cooke","given":"Steven","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":781242,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70218301,"text":"70218301 - 2020 - Controls on debris‐flow initiation on burned and unburned hillslopes during an exceptional rainstorm in southern New Mexico, USA","interactions":[],"lastModifiedDate":"2021-03-08T12:38:06.42036","indexId":"70218301","displayToPublicDate":"2019-12-02T07:15:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Controls on debris‐flow initiation on burned and unburned hillslopes during an exceptional rainstorm in southern New Mexico, USA","docAbstract":"AbstractUsing observations from 688 debris flows, we analyse the hydrologic and landscape characteristics that influenced debris‐flow initiation mechanisms and locations in a watershed that had been partially burned by the 2012 Whitewater‐Baldy Complex Fire in the Gila Mountains, southern New Mexico. Debris flows can initiate due to different processes. Slopes can fail as discrete landslides and then become fluidized and move downstream as debris flows (landslide initiated) or progressive bulking of sediment from a distributed area can become channelized and concentrated as it moves downslope (runoff generated). In this study, we have an unusual opportunity to investigate both types of debris‐flow initiation mechanisms in our observations of debris flows, triggered by an exceptional rainstorm in the autumn of 2013. Additionally, we compare our observations with those of a dataset of 1138 debris flows in the Colorado Front Range, triggered during the same weather system. We found that runoff‐generated debris flows dominated in burn areas, and runoff required to start these flows could be well characterized by the Shields stress. Landslide‐initiated debris flows were dominant in unburned areas. Debris‐flow densities were tied to total rainfall and precipitation intensities. Like the observations in the Colorado Front Range, debris‐flow initiation locations were found primarily in areas of relatively sparse vegetation on south‐facing slopes between 25 and 40°, and with upslope contributing areas less than 1000 m2. In terms of preferential locations for debris‐flow initiations, 2013 vegetation coverage, approximated by Green–Red Vegetation Index metrics, proved to be more influential than the 2012 burn‐severity designation. The uniformity of observations between our study area and those in the Colorado Front Range indicate that the underlying hydrologic and landscape patterns of the debris‐flow initiation locations documented in these studies could be applicable to the wider southwest and Rocky Mountain regions.
Sandbars are ubiquitous in sandy‐braided rivers throughout the world. In the Great Plains of the United States, recovery and expansion of emergent sandbar habitat (ESH) has been a priority in lowland rivers where the natural extent of sandbars has been degraded. Recovery efforts are aimed at protection of populations of the interior least tern (Sterna antillarum) and piping plover (Charadrius melodus). But quantitative observations of deposition and erosion dynamics of populations of sandbars across long segments of rivers are rare. We present a three‐part case study which used Bayesian regression models to examine relations between hydrology, channel morphology, and ESH responses in the Platte River, eastern Nebraska. Logistic regression indicates presence of ESH is positively related to the Parker, (1976) stability criterion and a gradient in sediment transport mode, and negatively related to presence of vegetation. Hierarchical linear regression modeling shows direct coupling between sandbar top‐surface height and formative flood magnitude, but the gap between formative flood stage and sandbar top‐surface increases with increasing discharge. Finally, linear regression modeling of sandbar erosion demonstrates rates of ESH erosion are on the order of 10−1 ha/day during high‐flow periods and 10−2 during low‐flow periods, but sandbar persistence is largely a function of sandbar starting size. The collective observations highlight the importance of large floods (>3‐year recurrence) in creating very large sandbars that persist as high‐quality ESH over periods of years whereas lower‐magnitude, more‐frequent flood events create lower‐quality ESH that typically does not persist into the following nesting season.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR024107","usgsCitation":"Alexander, J., McElroy, B., Huzurbazar, S., Elliott, C.M., and Murr, M.L., 2020, Deposition potential and flow-response dynamics of emergent sandbars in a braided river: Water Resources Research, v. 56, no. 1, e2018WR024107, 23 p., https://doi.org/10.1029/2018WR024107.","productDescription":"e2018WR024107, 23 p.","ipdsId":"IP-098093","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":383680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nebraska","otherGeospatial":"Platte River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.11865234374999,\n 40.66397287638688\n ],\n [\n -95.8502197265625,\n 40.66397287638688\n ],\n [\n -95.8502197265625,\n 42.11859868281563\n ],\n [\n -99.11865234374999,\n 42.11859868281563\n ],\n [\n -99.11865234374999,\n 40.66397287638688\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Alexander, Jason S. 0000-0002-1602-482X","orcid":"https://orcid.org/0000-0002-1602-482X","contributorId":204220,"corporation":false,"usgs":false,"family":"Alexander","given":"Jason S.","affiliations":[{"id":39297,"text":"former U.S. Geological Survey employee","active":true,"usgs":false},{"id":36881,"text":"Department of Geology and Geophysics, University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":811168,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McElroy, Brandon","contributorId":198820,"corporation":false,"usgs":false,"family":"McElroy","given":"Brandon","affiliations":[],"preferred":false,"id":811169,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huzurbazar, Snehalata","contributorId":85903,"corporation":false,"usgs":false,"family":"Huzurbazar","given":"Snehalata","email":"","affiliations":[],"preferred":false,"id":811171,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elliott, Caroline M. 0000-0002-9190-7462 celliott@usgs.gov","orcid":"https://orcid.org/0000-0002-9190-7462","contributorId":2380,"corporation":false,"usgs":true,"family":"Elliott","given":"Caroline","email":"celliott@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":811172,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murr, Marissa L.","contributorId":252938,"corporation":false,"usgs":false,"family":"Murr","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":50476,"text":"Department of Geology and Geophysics, University of Wyoming, Laramie, Wyoming","active":true,"usgs":false}],"preferred":false,"id":811170,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208803,"text":"70208803 - 2020 - Hydrologic resilience from summertime fog and recharge: A case study for coho salmon recovery planning","interactions":[],"lastModifiedDate":"2020-03-02T09:50:46","indexId":"70208803","displayToPublicDate":"2019-11-20T09:45:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic resilience from summertime fog and recharge: A case study for coho salmon recovery planning","docAbstract":"Fog and low cloud cover (FLCC) and late summer recharge increase stream baseflow and decrease stream temperature during arid Mediterranean climate summers, which benefits salmon especially under climate warming conditions. The potential to discharge cool water to streams during the late summer (hydrologic capacity; HC) furnished by FLCC and recharge were mapped for the 299 subwatersheds ranked Core, Phase 1, or Phase 2 under the National Marine Fisheries Service Recovery Plan that prioritized restoration and threat abatement action for endangered Central California Coast Coho Salmon evolutionarily significant unit. Two spatially continuous gridded datasets were merged to compare HC: average hrs/day FLCC, a new dataset derived from a decade of hourly National Weather Satellite data, and annual average mm recharge from the USGS Basin Characterization Model. Two use‐case scenarios provide examples of incorporating FLCC‐driven HC indices into long‐term recovery planning. The first, a thermal analysis under future climate, projected 65% of the watershed area for 8–19 coho population units as thermally inhospitable under two global climate models and identified several units with high resilience (high HC under the range of projected warming conditions). The second use case investigated HC by subwatershed rank and coho population, and identified three population units with high HC in areas ranked Phase 1 and 2 and low HC in Core. Recovery planning for cold‐water fish species would benefit by including FLCC in vulnerability analyses.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12811","usgsCitation":"Torregrosa, A.A., Flint, L.E., and Flint, A.L., 2020, Hydrologic resilience from summertime fog and recharge: A case study for coho salmon recovery planning: Journal of the American Water Resources Association, v. 56, no. 1, p. 134-160, https://doi.org/10.1111/1752-1688.12811.","productDescription":"27 p.","startPage":"134","endPage":"160","ipdsId":"IP-095384","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":458480,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12811","text":"Publisher Index Page"},{"id":372761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.18945312500001,\n 41.96765920367816\n ],\n [\n -128.0126953125,\n 38.39333888832238\n ],\n [\n -122.9150390625,\n 34.08906131584994\n ],\n [\n -117.79541015625001,\n 36.82687474287728\n ],\n [\n -124.18945312500001,\n 41.96765920367816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-11-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Torregrosa, Alicia A. 0000-0001-7361-2241 atorregrosa@usgs.gov","orcid":"https://orcid.org/0000-0001-7361-2241","contributorId":3471,"corporation":false,"usgs":true,"family":"Torregrosa","given":"Alicia","email":"atorregrosa@usgs.gov","middleInitial":"A.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":783455,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783456,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783457,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216085,"text":"70216085 - 2020 - Isotopic and geochemical assessment of the sensitivity of groundwater resources of Guam, Mariana Islands, to intra- and inter-annual variations in hydroclimate","interactions":[],"lastModifiedDate":"2020-12-14T14:06:39.692747","indexId":"70216085","displayToPublicDate":"2019-11-04T14:38:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Isotopic and geochemical assessment of the sensitivity of groundwater resources of Guam, Mariana Islands, to intra- and inter-annual variations in hydroclimate","docAbstract":"Assessing the sensitivity of groundwater systems to hydroclimate variability is critical to\nsustainable management of the water resources of Guam, US territory. We assess spatial and\ntemporal variability of isotopic and geochemical compositions of vadose and phreatic\ngroundwater sampled from cave drip sites and production wells, respectively, to better\nunderstand the vulnerability of the freshwater lens on Guam to variability in hydroclimate. We\nindependently evaluate the existing conceptual model of the Northern Guam Lens Aquifer that is largely based on physical, as opposed to geochemical, observations. Sampling was conducted from 2008 to 2015, over which rainfall gradually increased. Major ion geochemistry and Sr isotope values of groundwater show varying influence from soil, limestone bedrock, and\nseawater. Geochemical modeling that can explain spatial variability in groundwater Na+ and\nMg2+ concentrations and Sr/Ca and 87Sr/86 Sr values indicates that groundwater compositions are dominantly controlled by mixing of freshwater with seawater and water-rock interaction.\nDifferences between amount-weighted annual average precipitation δ18 O values and groundwater\nδ18 O values indicate a recharge bias toward the wet season, consistent with other tropical\ncarbonate island aquifer settings. Intra- and inter-annual variations in Na+ concentrations and\nδ18 O values in groundwater reflect sensitivity of recharge to seasonal variations in rainfall\namount and changes in annual rainfall amounts. Our results indicate the influence of multiple\nmodes of recharge on groundwater compositions and spatial variability in the sensitivity of\ngroundwater to seawater mixing. This sensitivity of the freshwater lens points to the vulnerability\nof groundwater resources to changes in recharge associated with climate, land-use change, and\nincreases in population.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.10.049","usgsCitation":"Beal, L., Wong, C.I., Bautista, K.K., Jenson, J.W., Banner, J.L., Lander, M.A., Gingerich, S.B., Partin, J.W., Hardt, B., and van Oort, N., 2020, Isotopic and geochemical assessment of the sensitivity of groundwater resources of Guam, Mariana Islands, to intra- and inter-annual variations in hydroclimate: Journal of Hydrology, v. 568, p. 174-183, https://doi.org/10.1016/j.jhydrol.2018.10.049.","productDescription":"10 p.","startPage":"174","endPage":"183","ipdsId":"IP-097993","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":380175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Guam, Mariana Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 143.50341796875,\n 12.683214911818666\n ],\n [\n 146.95312499999997,\n 12.683214911818666\n ],\n [\n 146.95312499999997,\n 16.088042220148818\n ],\n [\n 143.50341796875,\n 16.088042220148818\n ],\n [\n 143.50341796875,\n 12.683214911818666\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"568","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Beal, Lakin","contributorId":244457,"corporation":false,"usgs":false,"family":"Beal","given":"Lakin","email":"","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":803988,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wong, Corinne I.","contributorId":218689,"corporation":false,"usgs":false,"family":"Wong","given":"Corinne","email":"","middleInitial":"I.","affiliations":[{"id":39889,"text":"Environmental Science Institute, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":803989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bautista, Kaylyn K","contributorId":244458,"corporation":false,"usgs":false,"family":"Bautista","given":"Kaylyn","email":"","middleInitial":"K","affiliations":[{"id":39888,"text":"University of Guam, Water and Environmental Research Institute of the Western Pacific","active":true,"usgs":false}],"preferred":false,"id":803990,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jenson, John W.","contributorId":218688,"corporation":false,"usgs":false,"family":"Jenson","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":39888,"text":"University of Guam, Water and Environmental Research Institute of the Western Pacific","active":true,"usgs":false}],"preferred":false,"id":803991,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Banner, Jay L.","contributorId":218690,"corporation":false,"usgs":false,"family":"Banner","given":"Jay","email":"","middleInitial":"L.","affiliations":[{"id":39890,"text":"University of Texas at Austin, Jackson School of Geosciences","active":true,"usgs":false}],"preferred":false,"id":803992,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lander, Mark A","contributorId":244459,"corporation":false,"usgs":false,"family":"Lander","given":"Mark","email":"","middleInitial":"A","affiliations":[{"id":39888,"text":"University of Guam, Water and Environmental Research Institute of the Western Pacific","active":true,"usgs":false}],"preferred":false,"id":803993,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gingerich, Stephen B. 0000-0002-4381-0746 sbginger@usgs.gov","orcid":"https://orcid.org/0000-0002-4381-0746","contributorId":1426,"corporation":false,"usgs":true,"family":"Gingerich","given":"Stephen","email":"sbginger@usgs.gov","middleInitial":"B.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803994,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Partin, Judson W.","contributorId":203459,"corporation":false,"usgs":false,"family":"Partin","given":"Judson","email":"","middleInitial":"W.","affiliations":[{"id":36624,"text":"Institute for Geophysics, Jackson School of Geosciences, University of Texas at Austin, J. J. Pickle Research Campus, Building 196, 10100 Burnet Road (R2200), Austin, Texas 78758, USA","active":true,"usgs":false}],"preferred":false,"id":803995,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hardt, Ben","contributorId":244460,"corporation":false,"usgs":false,"family":"Hardt","given":"Ben","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":803996,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"van Oort, N.H.","contributorId":244521,"corporation":false,"usgs":false,"family":"van Oort","given":"N.H.","email":"","affiliations":[],"preferred":false,"id":804098,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70209553,"text":"70209553 - 2020 - Change points in annual peak streamflows: Method comparisons and historical change points in the United States","interactions":[],"lastModifiedDate":"2020-05-04T17:54:54.253292","indexId":"70209553","displayToPublicDate":"2019-11-02T07:59:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Change points in annual peak streamflows: Method comparisons and historical change points in the United States","docAbstract":"Change-point, or step-trend, detection is an active area of research in statistics and an area of great interest in hydrology because change points may be evidence of natural or anthropogenic changes in climatic, hydrologic, or landscape processes. A common change-point technique is the Pettitt test; however, many change-point methods are now available and testing of methods has been limited. This study investigated eight methods for detecting change points in the location (central tendency, seven methods) and scale (dispersion or spread, one method) of annual peak streamflows, using simulated data with and without change points, and peak-streamflow series from basins with known large additions of reservoir storage. Parametric methods tested, including a Bayesian one, did not perform well, even when transforming peak streamflows to approximate normality by using logarithms. Nonparametric methods other than the Pettitt test allow for more than one change point but have an unacceptable number of false positives. Based on the results of our methods comparisons, we used the Pettitt and the Mood tests to find change points in location and scale, respectively, in thousands of streamgage records in the conterminous United States. Change points in location (median) and scale are abundant, with the changes in median peak streamflow showing regional patterns, as well as a strong increased streamflow signal around 1970. The changes in scale of peak streamflows are dominated more by temporal than spatial patterns; more streamgages had decreases in scale in earlier decades than recent decades and more streamgages had increases in scale occurring in recent decades than earlier decades.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.124307","collaboration":"","usgsCitation":"Ryberg, K.R., Hodgkins, G.A., and Dudley, R., 2020, Change points in annual peak streamflows: Method comparisons and historical change points in the United States: Journal of Hydrology, v. 583, https://doi.org/10.1016/j.jhydrol.2019.124307.","productDescription":"124307, 13 p.","startPage":"","ipdsId":"IP-098428","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":373948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": 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,{"id":70208374,"text":"70208374 - 2020 - Hydrologic modeling for flow-ecology science in the Southeastern United States and Puerto Rico","interactions":[],"lastModifiedDate":"2020-02-05T17:51:21","indexId":"70208374","displayToPublicDate":"2019-11-01T17:50:59","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":32,"text":"General Technical Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"SRS-246","title":"Hydrologic modeling for flow-ecology science in the Southeastern United States and Puerto Rico","docAbstract":"An understanding of the applicability and utility of hydrologic models is critical to support the effective management of water resources throughout the Southeastern United States (SEUS) and Puerto Rico (PR). Hydrologic models have the capacity to provide an estimate of the quantity of available water at ungauged locations (i.e., areas of the country where a U.S. Geological Survey [USGS] continuous record gauge is not installed) and provide the baseline flow information necessary to develop the linkages between water availability and characteristics of streamflow that support ecological communities (i.e., support the development of flow-ecology response models). This report inventories and then directly examines and compares a subset of hydrologic models used to estimate streamflow at a number of gauged basins across the SEUS and PR. This effort was designed to evaluate, quantify, and compare the magnitude of error and to investigate the potential causes of error associated with predicted streamflows from seven hydrologic models of varying complexity and calibration strategy. This was accomplished by computing and then comparing classical hydrologic model fit statistics (e.g., mean bias, coefficient of determination [R2], root mean squared error [RMSE], Nash-Sutcliffe Efficiency [NSE]) and understanding the bias in the prediction in these and a subset of ecologically relevant flow metrics (ERFMs). Additionally, streamflow predictions from a larger regional-scale hydrologic model were compared to those of several fine-scale hydrologic models under a range of hypothetical climate change scenarios to determine the range of predicted streamflow responses to fixed climate perturbations. A pilot study was conducted using predicted streamflow and boosted regression trees to develop a set of predictive flow-ecology response models to assess the potential change in fish species richness in the North Carolina Piedmont under several scenarios of water availability change. This report is intended to provide a general assessment of all the tools and techniques available to support hydrologic modeling for flow-ecology science in the SEUS and PR. It is our hope that the approach used herein to understand differences in streamflow predictions among a subset of hydrologic models that have been applied in the SEUS for developing flow-ecology response models will provide water resource managers and stakeholders with an informed pathway for developing the capacity to link streamflow and ecological response and an understanding of some of the limitations associated with these type of modeling efforts.
","language":"English","publisher":"U.S. Department of Agriculture Forest Service","usgsCitation":"Caldwell, P.V., Kennen, J., Hain, E.F., Nelson, S.A., Sun, G., and McNulty, S., 2020, Hydrologic modeling for flow-ecology science in the Southeastern United States and Puerto Rico: General Technical Report SRS-246, iii, 77 p.","productDescription":"iii, 77 p.","ipdsId":"IP-098574","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":372111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":372091,"type":{"id":15,"text":"Index Page"},"url":"https://www.srs.fs.usda.gov/pubs/59109"}],"country":"United States","state":"Alabama, Florida, Georgia, Mississippi, North Carolina, Puerto Rico, South Carolina, Tennessee, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.06640625,\n 30.44867367928756\n ],\n [\n -85.25390625,\n 29.611670115197377\n ],\n [\n -84.287109375,\n 29.99300228455108\n ],\n [\n -82.880859375,\n 28.998531814051795\n ],\n [\n -81.298828125,\n 30.675715404167743\n ],\n [\n -78.31054687499999,\n 33.50475906922609\n ],\n [\n -75.849609375,\n 34.66935854524543\n ],\n [\n -75.673828125,\n 36.10237644873644\n ],\n [\n -76.552734375,\n 36.527294814546245\n ],\n [\n -80.068359375,\n 36.66841891894786\n ],\n [\n -80.771484375,\n 37.3002752813443\n ],\n [\n -82.08984375,\n 37.3002752813443\n ],\n [\n -83.84765625,\n 36.4566360115962\n ],\n [\n -87.5390625,\n 35.60371874069731\n ],\n [\n -89.736328125,\n 34.813803317113155\n ],\n [\n -89.56054687499999,\n 30.44867367928756\n ],\n [\n -88.06640625,\n 30.44867367928756\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.2747802734375,\n 17.868975338932746\n ],\n [\n -65.23681640625,\n 17.868975338932746\n ],\n [\n -65.23681640625,\n 18.531700307384043\n ],\n [\n -67.2747802734375,\n 18.531700307384043\n ],\n [\n -67.2747802734375,\n 17.868975338932746\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Caldwell, Peter V.","contributorId":222249,"corporation":false,"usgs":false,"family":"Caldwell","given":"Peter","email":"","middleInitial":"V.","affiliations":[{"id":39172,"text":"USDA Forest Service, Center for Forest Watershed Science, Coweeta Hydrologic Laboratory, Otto, NC, USA","active":true,"usgs":false}],"preferred":false,"id":781654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kennen, Jonathan G. 0000-0002-5426-4445 jgkennen@usgs.gov","orcid":"https://orcid.org/0000-0002-5426-4445","contributorId":574,"corporation":false,"usgs":true,"family":"Kennen","given":"Jonathan G.","email":"jgkennen@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781653,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hain, Ernie F.","contributorId":141247,"corporation":false,"usgs":false,"family":"Hain","given":"Ernie","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":781655,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, Stacy A.C.","contributorId":222250,"corporation":false,"usgs":false,"family":"Nelson","given":"Stacy","email":"","middleInitial":"A.C.","affiliations":[{"id":39171,"text":"Center for Geospatial Analytics, Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, 27695, USA","active":true,"usgs":false}],"preferred":false,"id":781656,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sun, Ge","contributorId":145893,"corporation":false,"usgs":false,"family":"Sun","given":"Ge","email":"","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":781657,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNulty, Steven G.","contributorId":222251,"corporation":false,"usgs":false,"family":"McNulty","given":"Steven G.","affiliations":[{"id":39173,"text":"USDA Forest Service, Eastern Forest Environmental Threat Assessment Center, Raleigh, NC, USA","active":true,"usgs":false}],"preferred":false,"id":781658,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224286,"text":"70224286 - 2020 - Predictive multi-scale occupancy models at range-wide extents: Effects of habitat and human disturbance on distributions of wetland birds","interactions":[],"lastModifiedDate":"2021-09-20T12:56:45.40967","indexId":"70224286","displayToPublicDate":"2019-10-21T07:55:23","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Predictive multi-scale occupancy models at range-wide extents: Effects of habitat and human disturbance on distributions of wetland birds","docAbstract":"Predicting distributions is fundamental to ecology, yet hindered by spatially restricted sampling, scale-dependent relationships and detection error associated with field surveys. Predictive species distribution models (SDMs) are nonetheless vital for conservation of many species. We developed a framework for building predictive SDMs with multi-scale data and used it to develop range-wide breeding-season SDMs for 14 marsh bird species of concern.
USA.
We built SDMs using data from range-wide surveys conducted over 14 years, and habitat and disturbance covariates measured at multiple spatial scales. We built hierarchical occupancy models that included heterogeneity in detectability during sampling, and used Bayesian model selection to regulate model complexity (covariates and scales) based explicitly on spatial predictive abilities. We thus integrated model selection for optimizing out-of-sample prediction, range-wide sampling over broad conditions, multi-scale analyses and scale optimization, and species-specific detectability for a suite of wide-ranging species.
Distributions of marsh birds were affected by local wetland conditions, but also by agricultural, urban and hydrologic disturbances operating from local scales (100–500 m) to the watershed level. Variables measuring human disturbances improved prediction for most species, and every species was affected by attributes at >1 scale. Five species showed evidence for continental-scale range contraction during the study.
We demonstrate how hierarchical occupancy models can be optimized for prediction across a species' range at the extent of a continent while also accounting for imperfect detection, and thus describe a generalizable approach that can be used for any species. We provide the first data-driven, empirical SDMs built at the range-wide extent for most of our 14 study species and demonstrate that previous studies focused on local distributions and the effects of fine-scale wetland vegetation missed important broadscale drivers of occupancy for marsh birds.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.12995","usgsCitation":"Stevens, B.S., and Conway, C.J., 2020, Predictive multi-scale occupancy models at range-wide extents: Effects of habitat and human disturbance on distributions of wetland birds: Diversity and Distributions, v. 26, no. 1, p. 34-48, https://doi.org/10.1111/ddi.12995.","productDescription":"15 p.","startPage":"34","endPage":"48","ipdsId":"IP-105638","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":458587,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.12995","text":"Publisher Index Page"},{"id":389474,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"1","noUsgsAuthors":false,"publicationDate":"2019-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Bryan S.","contributorId":171809,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":823459,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":823458,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70206418,"text":"70206418 - 2020 - Low streamflow trends at human-impacted and reference basins in the United States","interactions":[],"lastModifiedDate":"2019-11-04T14:42:50","indexId":"70206418","displayToPublicDate":"2019-10-18T14:36:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Low streamflow trends at human-impacted and reference basins in the United States","docAbstract":"We present a continent-scale exploration of trends in annual 7-day low streamflows at 2482 U.S. Geological Survey streamgages across the conterminous United States over the past 100, 75, and 50 years (1916–2015, 1941–2015 and 1966–2015). We used basin characteristics to identify subsets of study basins representative of reference basins with streamflow relatively free from human effects (n = 259), and predominantly agricultural basins (n = 78), regulated basins (n = 220), and urban basins (n = 121). Trend significance was computed using the Mann-Kendall test considering short- and long-term persistence. Lag-one autocorrelation tests of detrended 7-day low streamflows for all gage classes show that time-series independence is not an appropriate assumption for annual low streamflow data at many basins. Among all study gages, upward trends (wetter conditions) in 7-day low streamflows outnumbered downward trends (drier conditions) approximately 2–1 for the 75- and 100-year trend periods—50-year trends indicated roughly equal numbers of increases and decreases. Increases in 7-day low streamflow were consistently observed for all time periods throughout much of the northeastern quadrant of the conterminous U.S. including western New England and the Mid-Atlantic, the southeastern Great Lakes basin, northern Ohio River basin, and the Upper Mississippi River and eastern Missouri River basins. Decreases in 7-day low streamflow were consistently observed for all time periods at many gages in the southeastern U.S. and in the northwestern U.S. in much of Idaho and northwestern Washington. Overall, we observed greater percentages of statistically significant trends at gages with human-induced influences than at reference gages. Low-flow trends at agricultural gages were regionally consistent with trends at reference gages. Regulated basins had many statistically significant upward trends for all three time periods tested, which may be attributed in part to substantial increases in dam-related storage prior to 1970. Urban gages had the greatest percentage of significant decreases in 7-day low flows compared to all other gage classes even though most urban gages saw upward trends in mean annual flows. Urban gages also had the greatest percentage of significant increases in low flows second only to regulated gages, highlighting that urban development can increase or decrease low streamflows depending on the basin-specific development.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2019.124254","usgsCitation":"Dudley, R., Hirsch, R.M., Archfield, S.A., Blum, A., and Renard, B., 2020, Low streamflow trends at human-impacted and reference basins in the United States: Journal of Hydrology, v. 580, 124254, 13 p., https://doi.org/10.1016/j.jhydrol.2019.124254.","productDescription":"124254, 13 p.","ipdsId":"IP-098641","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":458591,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2019.124254","text":"Publisher Index 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0000-0002-0934-0568","orcid":"https://orcid.org/0000-0002-0934-0568","contributorId":220211,"corporation":false,"usgs":true,"family":"Dudley","given":"Robert W.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":774479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hirsch, Robert M. 0000-0002-4534-075X rhirsch@usgs.gov","orcid":"https://orcid.org/0000-0002-4534-075X","contributorId":2005,"corporation":false,"usgs":true,"family":"Hirsch","given":"Robert","email":"rhirsch@usgs.gov","middleInitial":"M.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":774480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Archfield, Stacey A. 0000-0002-9011-3871 sarch@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-3871","contributorId":1874,"corporation":false,"usgs":true,"family":"Archfield","given":"Stacey","email":"sarch@usgs.gov","middleInitial":"A.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":774481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blum, Annalise G.","contributorId":193846,"corporation":false,"usgs":false,"family":"Blum","given":"Annalise G.","affiliations":[],"preferred":false,"id":774482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Renard, Benjamin","contributorId":177291,"corporation":false,"usgs":false,"family":"Renard","given":"Benjamin","email":"","affiliations":[],"preferred":false,"id":774483,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223156,"text":"70223156 - 2020 - PFHydro: A new watershed-scale model for post-fire runoff simulation","interactions":[],"lastModifiedDate":"2021-08-12T12:16:02.699645","indexId":"70223156","displayToPublicDate":"2019-10-11T07:09:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"PFHydro: A new watershed-scale model for post-fire runoff simulation","docAbstract":"Runoff increases after wildfires that burn vegetation and create a condition of soil-water repellence (SWR). A new post-fire watershed hydrological model, PFHydro, was created to explicitly simulate vegetation interception and SWR effects for four burn severity categories: high, medium, low severity and unburned. The model was applied to simulate post-fire runoff from the Upper Cache Creek Watershed in California, USA. Nash–Sutcliffe modeling efficiency (NSE) was used to assess model performance. The NSE was 0.80 and 0.88 for pre-fire water years (WY) 2000 and 2015, respectively. NSE was 0.88 and 0.93 for WYs 2016 (first year post-fire) and 2017 respectively. The simulated percentage of surface runoff in total runoff of WY 2016 was about six times that of pre-fire WY 2000 and three times that of WY 2015. The modeling results suggest that SWR is an important factor for post-fire runoff generation. The model was successful at simulating SWR behavior.