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Understanding the habitat associations and distributions of rare species is important to inform management and policy decisions. Cambarus (Erebicambarus) maculatus  Hobbs & Pflieger, 1988, the freckled crayfish, and Faxonius (Billecambarus) harrisonii (Faxon, 1884), the belted crayfish, are two of Missouri’s endemic crayfish species. Both species are listed as Vulnerable (S3) on Missouri’s Species and Communities of Conservation Concern Checklist due to their limited range within the Meramec River drainage (MRD) and the impact of anthropogenic activities therein. Their distributional overlap offers an opportunity for multi-species research to address gaps in information required for conservation. We sampled 140 sites throughout the MRD during the summers of 2017 and 2018 for crayfishes and associated habitat variables, which we related to crayfish presence in an occupancy modeling framework. We found that C. maculatus occupancy was associated with larger stream size, boulder substrate, dolomite lithology, aquatic vegetation beds, dissolved oxygen, and pool mesohabitat. Faxonius harrisonii occupancy increased with boulder substrate, aquatic vegetation beds, the presence of C. maculatus, and decreased in third-order streams. We also expanded the known range for both species within the MRD. Range estimates (watershed area) for C. maculatus and F. harrisonii were 4,347 km2 and 3,690 km2, respectively. This study demonstrates the importance of targeted rather than opportunistic sampling for species distribution.

","language":"English","publisher":"Oxford University Press","doi":"10.1093/jcbiol/ruaa033","usgsCitation":"Chilton, J., Rosenberger, A.E., and DiStefano, R., 2020, Habitat associations and distributions of two endemic crayfishes, Cambarus (Erebicambarus) maculatus Hobbs & Pflieger, 1988 and Faxonius (Billecambarus) harrisonii (Faxon, 1884) (Decapoda: Astacoidea: Cambaridae), in the Meramec River drainage, Missouri, USA, v. 40, no. 4, p. 351-363, https://doi.org/10.1093/jcbiol/ruaa033.","productDescription":"13 p.","startPage":"351","endPage":"363","ipdsId":"IP-123414","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jcbiol/ruaa033","text":"Publisher Index Page"},{"id":396426,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","otherGeospatial":"Meramec River drainage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.90887451171875,\n 37.54239958054064\n ],\n [\n -90.428466796875,\n 37.54239958054064\n ],\n [\n -90.428466796875,\n 38.59970036588819\n ],\n [\n -91.90887451171875,\n 38.59970036588819\n ],\n [\n -91.90887451171875,\n 37.54239958054064\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Chilton, J.","contributorId":280068,"corporation":false,"usgs":false,"family":"Chilton","given":"J.","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":835981,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberger, Amanda E. 0000-0002-5520-8349 arosenberger@usgs.gov","orcid":"https://orcid.org/0000-0002-5520-8349","contributorId":5581,"corporation":false,"usgs":true,"family":"Rosenberger","given":"Amanda","email":"arosenberger@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":835983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DiStefano, Robert J.","contributorId":213268,"corporation":false,"usgs":false,"family":"DiStefano","given":"Robert J.","affiliations":[{"id":16971,"text":"Missouri Department of Conservation","active":true,"usgs":false}],"preferred":false,"id":835982,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212869,"text":"70212869 - 2020 - 3D fault architecture controls the dynamism of earthquake swarm","interactions":[],"lastModifiedDate":"2020-09-02T01:05:58.372079","indexId":"70212869","displayToPublicDate":"2020-06-19T20:03:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"3D fault architecture controls the dynamism of earthquake swarm","docAbstract":"

The vibrant evolutionary patterns made by earthquake swarms are incompatible with standard, effectively two-dimensional (2D) models for general fault architecture. We leverage advances in earthquake monitoring with a deep-learning algorithm to image a fault zone hosting a 4-year-long swarm in southern California. We infer that fluids are naturally injected into the fault zone from below and diffuse through strike-parallel channels while triggering earthquakes. A permeability barrier initially limits up-dip swarm migration but ultimately is circumvented. This enables fluid migration within a shallower section of the fault with fundamentally different mechanical properties. Our observations provide high-resolution constraints on the processes by which swarms initiate, grow, and arrest. These findings illustrate how swarm evolution is strongly controlled by 3D variations in fault architecture.

","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/science.abb0779","usgsCitation":"Ross, Z., Cochran, E.S., Trugman, D., and Smith, J., 2020, 3D fault architecture controls the dynamism of earthquake swarm: Science, v. 368, no. 6497, p. 1357-1361, https://doi.org/10.1126/science.abb0779.","productDescription":"5 p.","startPage":"1357","endPage":"1361","ipdsId":"IP-118187","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":456344,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.osti.gov/biblio/1739984","text":"External Repository"},{"id":378083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"368","issue":"6497","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ross, Z.","contributorId":215300,"corporation":false,"usgs":false,"family":"Ross","given":"Z.","email":"","affiliations":[{"id":13711,"text":"Caltech","active":true,"usgs":false}],"preferred":false,"id":797732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":797733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Trugman, D.","contributorId":173991,"corporation":false,"usgs":false,"family":"Trugman","given":"D.","email":"","affiliations":[{"id":15303,"text":"University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":797734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Jonathan D.","contributorId":239737,"corporation":false,"usgs":false,"family":"Smith","given":"Jonathan D.","affiliations":[],"preferred":false,"id":797735,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210923,"text":"70210923 - 2020 - Hurricane Sandy effects on coastal marsh elevation change","interactions":[],"lastModifiedDate":"2020-09-24T14:26:25.740789","indexId":"70210923","displayToPublicDate":"2020-06-19T09:19:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Hurricane Sandy effects on coastal marsh elevation change","docAbstract":"High-magnitude storm events such as Hurricane Sandy are powerful agents of geomorphic change in coastal marshes, potentially altering their surface elevation trajectories. But how do a storm’s impacts vary across a large region spanning a variety of wetland settings and storm exposures and intensities. We determined the short-term impacts of Hurricane Sandy at 223 surface elevation table–marker horizon stations in estuarine marshes located across the northeast region of the United States by comparing post-storm surface elevation change with pre-storm elevation trends. We hypothesized that the storm’s effect on marsh elevation trends would be influenced by position relative to landfall (right or left) and distance from landfall. The structural equation model presented predicts that marshes located to the left of landfall were more likely to experience an elevation gain greater than expected, and this positive deviation from pre-storm elevation trends tended to have a greater magnitude than those experiencing negative deviations (elevation loss), potentially due to greater sediment deposition. The magnitude of negative deviations from elevation change in marshes to the right of landfall was greater than for positive deviations, with a greater effect in marshes within 200 km of landfall, potentially from the extent and magnitude of storm surge. Overall, results provide an integrated picture of how storm characteristics combined with the local wetland setting are important to a storm’s impact on surface elevation, and that the surface elevation response can vary widely among sites across a region impacted by the same storm.","language":"English","publisher":"Springer","doi":"10.1007/s12237-020-00758-5","usgsCitation":"Yeates, A.G., Grace, J., Olker, J.H., Guntenspergen, G.R., Cahoon, D., Adamowicz, S.C., Anisfeld, S.C., Barrett, N., Benzecry, A., Blum, L.K., Christian, R.T., Grzyb, J., Kracauer Hartig, E., Hines Leo, K., Lerberg, S., Lynch, J.C., Maher, N., Megonigal, J., Reay, W.G., Siok, D., Starke, A., Turner, V., and Warren, S., 2020, Hurricane Sandy effects on coastal marsh elevation change: Estuaries and Coasts, v. 43, p. 1640-1657, https://doi.org/10.1007/s12237-020-00758-5.","productDescription":"18 p.","startPage":"1640","endPage":"1657","ipdsId":"IP-104537","costCenters":[{"id":531,"text":"Patuxent Wildlife Research 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Keys to Baird’s Sparrow (Centronyx bairdii) management are providing native or tame grasslands with moderately deep litter, controlling excessive grazing, and curtailing shrub encroachment. Baird’s Sparrows have been reported to use habitats with less than or equal to (≤) 101 centimeters (cm) average vegetation height, 3–46 cm visual obstruction reading (VOR), 15–71 percent grass cover, 5–25 percent forb cover, ≤50 percent shrub cover, less than (<) 44 percent bare ground, 10–63 percent litter cover, and ≤21 cm litter depth.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842HH","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Nenneman, M.P., and Euliss, B.R., 2020, The effects of management practices on grassland birds—Baird’s Sparrow (Centronyx bairdii), chap. HH of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 21 p., https://doi.org/10.3133/pp1842HH.","productDescription":"v, 21 p.","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097125","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":375658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/hh/coverthb.jpg"},{"id":375659,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/hh/pp1842hh.pdf","text":"Report","size":"2.22 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–HH"}],"contact":"

Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-06-18","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Shaffer, Jill A. 0000-0003-3172-0708","orcid":"https://orcid.org/0000-0003-3172-0708","contributorId":223126,"corporation":false,"usgs":true,"family":"Shaffer","given":"Jill A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Igl, Lawrence D. 0000-0003-0530-7266","orcid":"https://orcid.org/0000-0003-0530-7266","contributorId":214801,"corporation":false,"usgs":true,"family":"Igl","given":"Lawrence D.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Douglas H. 0000-0002-7778-6641 douglas_h_johnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7778-6641","contributorId":1387,"corporation":false,"usgs":true,"family":"Johnson","given":"Douglas","email":"douglas_h_johnson@usgs.gov","middleInitial":"H.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":791004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sondreal, Marriah L.","contributorId":73532,"corporation":false,"usgs":true,"family":"Sondreal","given":"Marriah","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":791005,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldade, Christopher M.","contributorId":90668,"corporation":false,"usgs":true,"family":"Goldade","given":"Christopher M.","affiliations":[],"preferred":false,"id":791006,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nenneman, Melvin P.","contributorId":190777,"corporation":false,"usgs":false,"family":"Nenneman","given":"Melvin","email":"","middleInitial":"P.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":791007,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Euliss, Betty R.","contributorId":191881,"corporation":false,"usgs":false,"family":"Euliss","given":"Betty","email":"","middleInitial":"R.","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":791008,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70211531,"text":"70211531 - 2020 - Modified QuEChERS extraction for the analysis of young-of-year smallmouth bass using GC × GC-TOFMS","interactions":[],"lastModifiedDate":"2020-07-30T16:47:05.265837","indexId":"70211531","displayToPublicDate":"2020-06-18T11:43:45","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":763,"text":"Analytical Methods","active":true,"publicationSubtype":{"id":10}},"title":"Modified QuEChERS extraction for the analysis of young-of-year smallmouth bass using GC × GC-TOFMS","docAbstract":"

Signs of disease, such as external lesions, have been prevalent in smallmouth bass throughout the Susquehanna River Basin, USA. Previous targeted chemical studies in this system have identified known persistent organic pollutants, but a common explanatory link across multiple affected sites remains undetermined. A fast and robust extraction method that can be applied to young-of-year fish is needed to effectively screen for target and non-target compounds that may be impacting organism health. The quick, easy, cheap, effective, rugged, and safe (QuEChERS) extraction methodology was optimized to perform both targeted and non-targeted chemical analyses from a single extraction of whole young-of-year fish. Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) was used for extract analysis. Sample extraction was performed using the solvent ethyl acetate, followed by a two-step cleanup in which samples were frozen for lipid removal and subjected to dispersive solid phase extraction using Florisil. A sample of 21 young-of-year smallmouth bass collected from areas with disease and exhibiting different types of external lesions were evaluated for 233 target compounds. A total of 34 organic contaminants, including polychlorinated biphenyls, brominated diphenyl ethers, organochlorinated pesticides, and personal care products, were detected. Data from this sample set was then analyzed for non-targets. Using the Fisher ratio method and multivariate analysis, an additional 10 significant features were identified specific to either fish with visible lesions or with no visible disease characteristics.

","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/D0AY00880J","usgsCitation":"Teehan, P., Schall, M.K., Blazer, V., Gruber, B., and Dorman, F.L., 2020, Modified QuEChERS extraction for the analysis of young-of-year smallmouth bass using GC × GC-TOFMS: Analytical Methods, v. 12, p. 3697-3704, https://doi.org/10.1039/D0AY00880J.","productDescription":"8 p.","startPage":"3697","endPage":"3704","ipdsId":"IP-118177","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":376913,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","city":"Harrisburg","otherGeospatial":"Susquehanna River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.9379425048828,\n 40.20981886323868\n ],\n [\n -76.83631896972655,\n 40.20981886323868\n ],\n [\n -76.83631896972655,\n 40.30204709875983\n ],\n [\n -76.9379425048828,\n 40.30204709875983\n ],\n [\n -76.9379425048828,\n 40.20981886323868\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Teehan, Paige","contributorId":236874,"corporation":false,"usgs":false,"family":"Teehan","given":"Paige","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":794530,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schall, Megan K.","contributorId":115964,"corporation":false,"usgs":false,"family":"Schall","given":"Megan","email":"","middleInitial":"K.","affiliations":[{"id":17758,"text":"Pennsylvania State Univ.","active":true,"usgs":false}],"preferred":false,"id":794531,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794532,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gruber, Beate","contributorId":236875,"corporation":false,"usgs":false,"family":"Gruber","given":"Beate","email":"","affiliations":[{"id":47556,"text":"Research Institute for Chromatography, Belgium","active":true,"usgs":false}],"preferred":false,"id":794533,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dorman, Frank L","contributorId":236876,"corporation":false,"usgs":false,"family":"Dorman","given":"Frank","email":"","middleInitial":"L","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":794534,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212520,"text":"70212520 - 2020 - Can nutrient additions facilitate recovery of Pacific salmon?","interactions":[],"lastModifiedDate":"2020-10-12T17:18:17.323586","indexId":"70212520","displayToPublicDate":"2020-06-18T09:41:46","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6455,"text":"Canadian Journal Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Can nutrient additions facilitate recovery of Pacific salmon?","docAbstract":"

Multiple restoration actions have been implemented in response to declining salmon populations. Among these is the addition of salmon carcasses or artificial nutrients to mimic marine-derived nutrients historically provided by large spawning runs of salmon. A key assumption in this approach is that increased nutrients will catalyze salmon population growth. Although effects on aquatic ecosystems have been observed during treatments, it is unclear whether permanent population increases for salmon will occur. To test this assumption and address associated uncertainties, we linked a food web model with a salmon life cycle model to examine whether carcass additions in a river reach would improve conditions for salmon in the long term. Model results confirmed immediate increases in the biomass of periphyton, macroinvertebrates, and fish during carcass additions. In turn, juvenile salmon grew larger and experienced improved freshwater and smolt survival, which translated to a greater number of adults returning to spawn. However, once additions ceased, salmon abundance returned to pretreatment levels, which, based on our model, is owing to a combination of instream and out-of-basin factors. Overall, results of this work suggest that benefits during carcass and nutrient additions may not translate into persistent productivity of salmon unless additions are sustained indefinitely or other limiting factors are addressed.

","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0438","usgsCitation":"Benjamin, J.R., Bellmore, J.R., Whitney, E., and Dunham, J.B., 2020, Can nutrient additions facilitate recovery of Pacific salmon?: Canadian Journal Fisheries and Aquatic Sciences, v. 77, p. 1601-1611, https://doi.org/10.1139/cjfas-2019-0438.","productDescription":"11 p.","startPage":"1601","endPage":"1611","ipdsId":"IP-113407","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":500999,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/101959","text":"External Repository"},{"id":377655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alberta, British Columbia, Idaho, Montana, Nevada, Oregon, Utah, Washinton, Wyoming","otherGeospatial":"Columbia River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.13818359375,\n 45.27488643704891\n ],\n [\n -110.89599609375,\n 46.210249600187225\n ],\n [\n -112.43408203124999,\n 47.39834920035926\n ],\n [\n -115.57617187499999,\n 51.83577752045248\n ],\n [\n -118.125,\n 53.028000167735165\n ],\n [\n -120.73974609374999,\n 53.09402405506325\n ],\n [\n -121.48681640624999,\n 52.74959372674114\n ],\n [\n -119.5751953125,\n 50.90303283111257\n ],\n [\n -120.234375,\n 50.51342652633956\n ],\n [\n -120.84960937499999,\n 48.821332549646634\n ],\n [\n -121.28906250000001,\n 48.180738507303836\n ],\n [\n -121.33300781249999,\n 47.12995075666307\n ],\n [\n -123.92578125,\n 46.6795944656402\n ],\n [\n -123.85986328124999,\n 45.49094569262732\n ],\n [\n -122.6953125,\n 44.98034238084973\n ],\n [\n -122.607421875,\n 43.644025847699496\n ],\n [\n -121.83837890625,\n 42.32606244456202\n ],\n [\n -119.59716796875,\n 42.17968819665961\n ],\n [\n -118.45458984375,\n 43.32517767999296\n ],\n [\n -117.61962890624999,\n 42.69858589169842\n ],\n [\n -116.16943359374999,\n 41.393294288784865\n ],\n [\n -114.85107421875,\n 40.88029480552824\n ],\n [\n -112.96142578125,\n 42.06560675405716\n ],\n [\n -112.19238281249999,\n 42.779275360241904\n ],\n [\n -110.302734375,\n 43.37311218382002\n ],\n [\n -109.16015624999999,\n 43.94537239244209\n ],\n [\n -109.13818359375,\n 45.27488643704891\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":796664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bellmore, James R 0000-0002-5140-6460","orcid":"https://orcid.org/0000-0002-5140-6460","contributorId":195609,"corporation":false,"usgs":false,"family":"Bellmore","given":"James","email":"","middleInitial":"R","affiliations":[],"preferred":false,"id":796665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitney, Emily","contributorId":238834,"corporation":false,"usgs":false,"family":"Whitney","given":"Emily","affiliations":[{"id":16298,"text":"University of Alaska Southeast","active":true,"usgs":false}],"preferred":false,"id":796666,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":796667,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228727,"text":"70228727 - 2020 - Estimating the drivers of species distributions with opportunistic data using mediation analysis","interactions":[],"lastModifiedDate":"2022-02-17T15:15:00.528851","indexId":"70228727","displayToPublicDate":"2020-06-18T09:07:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the drivers of species distributions with opportunistic data using mediation analysis","docAbstract":"

Ecological occupancy modeling has historically relied on high-quality, low-quantity designed-survey data for estimation and prediction. In recent years, there has been a large increase in the amount of high-quantity, unknown-quality opportunistic data. This has motivated research on how best to combine these two data sources in order to optimize inference. Existing methods can be infeasible for large datasets or require opportunistic data to be located where designed-survey data exist. These methods map species occupancies, motivating a need to properly evaluate covariate effects (e.g., land cover proportion) on their distributions. We describe a spatial estimation method for supplementarily including additional opportunistic data using mediation analysis concepts. The opportunistic data mediate the effect of the covariate on the designed-survey data response, decomposing it into a direct and indirect effect. A component of the indirect effect can then be quickly estimated via regressing the mediator on the covariate, while the other components are estimated through a spatial occupancy model. The regression step allows for use of large quantities of opportunistic data that can be collected in locations with no designed-survey data available. Simulation results suggest that the mediated method produces an improvement in relative MSE when the data are of reasonable quality. However, when the simulated opportunistic data are poorly correlated with the true spatial process, the standard, unmediated method is still preferable. A spatiotemporal extension of the method is also developed for analyzing the effect of deciduous forest land cover on red-eyed vireo distribution in the southeastern United States and find that including the opportunistic data do not lead to a substantial improvement. Opportunistic data quality remains an important consideration when employing this method, as with other data integration methods.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3165","usgsCitation":"Huberman, D.B., Reich, B.J., Pacifici, K., and Collazo, J.A., 2020, Estimating the drivers of species distributions with opportunistic data using mediation analysis: Ecosphere, v. 11, no. 6, e03165, 13 p., https://doi.org/10.1002/ecs2.3165.","productDescription":"e03165, 13 p.","ipdsId":"IP-113854","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":456360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3165","text":"Publisher Index Page"},{"id":396100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"eastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.03125,\n 24.84656534821976\n ],\n [\n -66.09375,\n 24.84656534821976\n ],\n [\n -66.09375,\n 49.26780455063753\n ],\n [\n -97.03125,\n 49.26780455063753\n ],\n [\n -97.03125,\n 24.84656534821976\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Huberman, D. B.","contributorId":279615,"corporation":false,"usgs":false,"family":"Huberman","given":"D.","email":"","middleInitial":"B.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reich, B. J.","contributorId":279616,"corporation":false,"usgs":false,"family":"Reich","given":"B.","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pacifici, Krishna","contributorId":244494,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":835223,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":835224,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210681,"text":"sir20205045 - 2020 - Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16","interactions":[],"lastModifiedDate":"2020-06-18T14:21:59.738964","indexId":"sir20205045","displayToPublicDate":"2020-06-17T15:15:22","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5045","displayTitle":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16","title":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16","docAbstract":"

The city of Omaha, Nebraska, has a combined sewer system in some areas of the city. In Omaha, Nebr., a moderate amount of rainfall will lead to the combination of stormwater and untreated sewage or wastewater being discharged directly into the Missouri River and Papillion Creek and is called a combined sewer overflow (CSO) event. In 2009, the city of Omaha began the implementation of their Long Term Control Plan (LTCP) to mitigate the effects of CSOs on the Missouri River and Papillion Creek. As part of the LTCP, the city partnered with the U.S. Geological Survey (USGS) in 2012 to begin monitoring in the Missouri River. Since 2012, monthly discrete water-quality samples for many constituents have been collected from the Missouri River at four sites. At 3 of the 4 sites, water quality has been monitored continuously for selected constituents and physical properties. These discrete water-quality samples and continuous water-quality monitoring data (from July 2012 to 2020) have been collected to better understand the water quality of the Missouri River, how it is changing with time, how it changes upstream from the city of Omaha to downstream, and how it varies during base-flow conditions and during periods of runoff.

The purpose of this report is to document the development of Escherichia coli (E. coli) concentration models for these four Missouri River sites. Analysis was completed using the first 5 years of data (through 2016) to determine if the current approach is sufficient to meet future analysis goals and to understand if proposed models such as Load Estimator (LOADEST) models will be able to represent water-quality changes in the Missouri River.

Multiple linear regression models were developed to estimate E. coli concentration using LOADEST as implemented in the rloadest package in the R statistical software program. A set of explanatory variables, including streamflow and streamflow anomalies, precipitation, information about CSOs, and continuous water quality, were evaluated for potential inclusion in regression models. The best model at Missouri River at NP Dodge Park at Omaha, Nebr. (USGS station 412126095565201; hereafter “NP Dodge”) included basin explanatory variables of upstream antecedent precipitation index measured at Tekamah, Nebr.; decimal time; season; and turbidity. The best model at Missouri River at Freedom Park Omaha, Nebr. (USGS station 411636095535401; hereafter “Freedom Park”) included the same explanatory variables as the NP Dodge model with the addition of turbidity anomalies and flow anomalies. The best models at the two downstream sites (Missouri River near Council Bluffs, Iowa, USGS station 06610505 and Missouri River near La Platte, Nebr., USGS station 410333095530101) included the same explanatory variables as the Freedom Park model with the addition of local antecedent precipitation index as measured at Eppley Airport in Omaha, Nebr., and additional turbidity and flow anomalies. The final selected models were the best models given our modeling design constraint in which explanatory variables included in the model for the upstream site were included in the downstream models.

Explanatory variables currently (2020) being collected and included in the selected models through 2016 explained 64–75 percent of the variability of E. coli concentration in the Missouri River. Explaining 64–75 percent of the variability might be considered low when working with physical constituents (total nitrogen or sediment), but with the natural variability of biological constituents such as E. coli, the uncertainty of E. coli laboratory measurements, and the added complexity of modeling in a large drainage basin with multiple sources, these results are adequate and indicate that the explanatory variables being collected and models such as LOADEST can represent water-quality changes in the Missouri River for E. coli concentration from 2012 to 2016.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205045","collaboration":"Prepared in cooperation with the city of Omaha, Nebraska","usgsCitation":"Densmore, B.K., Hall, B.M., and Moser, M.T., 2020, Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: U.S. Geological Survey Scientific Investigations Report 2020–5045, 24 p., https://doi.org/10.3133/sir20205045.","productDescription":"Report: vi, 24 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-098296","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":375621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5045/coverthb.jpg"},{"id":375622,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5045/sir20205045.pdf","text":"Report","size":"9.38 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5045"},{"id":375623,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97S6WSV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Modeling Escherichia coli in the Missouri River near Omaha, Nebraska, 2012–16: Model Inputs and Outputs"}],"country":"United States","state":"Nebraska","city":"Omaha","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.23611450195312,\n 41.166249339092\n ],\n [\n -95.78155517578124,\n 41.15901221836655\n ],\n [\n -95.7843017578125,\n 41.37783904584602\n ],\n [\n -95.95321655273436,\n 41.37886950966323\n ],\n [\n -96.23611450195312,\n 41.37165592008984\n ],\n [\n -96.23611450195312,\n 41.166249339092\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-06-17","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Densmore, Brenda K. 0000-0003-2429-638X bdensmore@usgs.gov","orcid":"https://orcid.org/0000-0003-2429-638X","contributorId":4896,"corporation":false,"usgs":true,"family":"Densmore","given":"Brenda","email":"bdensmore@usgs.gov","middleInitial":"K.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790927,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Brent M. 0000-0003-3815-5158 bhall@usgs.gov","orcid":"https://orcid.org/0000-0003-3815-5158","contributorId":4547,"corporation":false,"usgs":true,"family":"Hall","given":"Brent","email":"bhall@usgs.gov","middleInitial":"M.","affiliations":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790928,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Moser, Matthew T. 0000-0002-4891-3381","orcid":"https://orcid.org/0000-0002-4891-3381","contributorId":94994,"corporation":false,"usgs":true,"family":"Moser","given":"Matthew","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":790929,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70210708,"text":"70210708 - 2020 - Comparing trends in modeled and observed streamflows at minimally altered basins in the United States","interactions":[],"lastModifiedDate":"2020-06-19T15:59:45.860026","indexId":"70210708","displayToPublicDate":"2020-06-17T09:43:47","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":"Comparing trends in modeled and observed streamflows at minimally altered basins in the United States","docAbstract":"We compared modeled and observed streamflow trends from 1984–2016 using five statistical transfer models and one deterministic, distributed-parameter, process-based model, for 26 flow metrics at 502 basins in the United States that are minimally influenced by development. We also looked at a measure of overall model fit and average bias. A higher percentage of basins, for all models, had relatively low trend differences between modeled and observed mean/ median flows than for very high or low flows such as the annual 1-day high and 7-day low flows. Mean-flow metrics also had the largest percentage of basins with relatively good overall model fit and low bias. The five statistical transfer models performed better at more basins than the process-based model. The overall model fit for all models, for mean and/or high flows, was correlated with one or more measures of basin precipitation or aridity. Our study and previous studies generally observed good model performance for high flows up to 90th or 95th percentile flows. However, we found model performance was substantially worse for more extreme flows, including 99th percentile and annual 1-day high flows; this shows the importance of including more extreme high flows in analyses of model performance.","language":"English","publisher":"MDPI","doi":"10.3390/w12061728","usgsCitation":"Hodgkins, G.A., Dudley, R., Russell, A.M., and LaFontaine, J.H., 2020, Comparing trends in modeled and observed streamflows at minimally altered basins in the United States: Water, v. 12, 1728, 20 p., https://doi.org/10.3390/w12061728.","productDescription":"1728, 20 p.","ipdsId":"IP-117596","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":456370,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12061728","text":"Publisher Index Page"},{"id":436926,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XT4WSP","text":"USGS data 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]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodgkins, Glenn A. 0000-0002-4916-5565 gahodgki@usgs.gov","orcid":"https://orcid.org/0000-0002-4916-5565","contributorId":2020,"corporation":false,"usgs":true,"family":"Hodgkins","given":"Glenn","email":"gahodgki@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":371,"text":"Maine Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791051,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dudley, Robert W. 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 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Starting on 3 May 2018, a series of eruptive fissures opened in Kīlauea Volcano’s lower East Rift Zone (LERZ). Over the course of the next 3 months, intense degassing accompanied lava effusion from these fissures. Here, we report on ground-based observations of the gas emissions associated with Kīlauea’s 2018 eruption. Visual observations combined with radiative transfer modeling show that ultraviolet light could not efficiently penetrate the gas and aerosol plume in the LERZ, complicating SO2 measurements by differential optical absorption spectroscopy (DOAS). By applying a statistical method that integrates a radiative transfer model with the DOAS retrievals, we were able to calculate sulfur dioxide (SO2) emission rates along with estimates of their uncertainty. We find that sustained SO2 emissions were highest in June and early July, when approximately 200 kt SO2 were emitted daily. At the 68% confidence interval, we estimate that 7.1–13.6 Mt SO2 were released from the LERZ during the entire May to September eruptive episode. Scaling our results with in situ measurements of plume composition, we calculate that 11–21 Mt H2O and 1.5–2.8 Mt CO2 were also emitted. The gas and aerosol emissions caused hazardous conditions in areas proximal to the active vents, but plume dispersion modeling shows that the eruption also significantly impacted air quality hundreds of kilometers downwind. Combined with petrologic studies of the erupted lavas, our measurements indicate that 1.1–2.3 km3 dense-rock equivalent of lava were erupted from the LERZ, which is approximately twice the concomitant collapse volume of the volcano’s summit.

","language":"English","publisher":"Springer","doi":"10.1007/s00445-020-01390-8","usgsCitation":"Kern, C., Lerner, A., Elias, T., Nadeau, P.A., Holland, L., Kelly, P.J., Werner, C., Clor, L., and Cappos, M., 2020, Quantifying gas emissions associated with the 2018 rift eruption of Kīlauea Volcano using ground-based DOAS measurements: Bulletin of Volcanology, v. 82, 55, 24 p., https://doi.org/10.1007/s00445-020-01390-8.","productDescription":"55, 24 p.","ipdsId":"IP-115197","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":436927,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LXBJF3","text":"USGS data release","linkHelpText":"Differential Optical Absorption Spectroscopy data acquired during the 2018 rift eruption of Kilauea Volcano"},{"id":376424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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0000-0001-9883-1475","orcid":"https://orcid.org/0000-0001-9883-1475","contributorId":215607,"corporation":false,"usgs":true,"family":"Cappos","given":"Michael","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":793002,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70212476,"text":"70212476 - 2020 - Assessing the value of removing earthquake-hazard-related epistemic uncertainties, exemplified using average annual loss in California","interactions":[],"lastModifiedDate":"2020-11-30T16:34:15.09351","indexId":"70212476","displayToPublicDate":"2020-06-17T09:05:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the value of removing earthquake-hazard-related epistemic uncertainties, exemplified using average annual loss in California","docAbstract":"

To aid in setting scientific research priorities, we assess the potential value of removing each of the epistemic uncertainties currently represented in the US Geological Survey California seismic-hazard model, using average annual loss (AAL) as the risk metric of interest. Given all the uncertainties, represented with logic-tree branches, we find a mean AAL of $3.94 billion. The modal value is 17.5% lower than the mean, and there is a 78% chance that the true AAL value is more than 10% away from the mean, and a 5% chance that it is a factor 2.1 greater or lower than the mean. We quantify the extent to which resolving each uncertainty improves the AAL estimate. The most influential branch is one that adds additional epistemic uncertainty to ground motion models, but others are found to be influential as well, such as the rate of M ≥ 5 events throughout the region. We discuss the broader implications of our findings, and note that the time dependence caused by spatiotemporal clustering can be much more influential on AAL than the epistemic uncertainties explored here.

","language":"English","publisher":"Sage Journals","doi":"10.1177/8755293020926185","usgsCitation":"Field, E., Milner, K.R., and Porter, K., 2020, Assessing the value of removing earthquake-hazard-related epistemic uncertainties, exemplified using average annual loss in California: Earthquake Spectra, v. 36, no. 4, p. 1912-1929, https://doi.org/10.1177/8755293020926185.","productDescription":"18 p.","startPage":"1912","endPage":"1929","ipdsId":"IP-117774","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":377560,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":1165,"corporation":false,"usgs":true,"family":"Field","given":"Edward H.","email":"field@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":796430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Milner, Kevin R.","contributorId":194141,"corporation":false,"usgs":false,"family":"Milner","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":796431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porter, 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1931–2016","docAbstract":"

The North Dakota hydrology manual prepared by the U.S. Department of Agriculture, Soil Conservation Service, presents methodologies primarily used for developing hydrology for onfarm conservation practices, watershed projects, Resource Conservation and Development project measures, and river basin studies. The manual includes data necessary for determining hydrologic factors and developing a design discharge for a given site and intended purpose. The U.S. Geological Survey, in cooperation with the North Dakota Natural Resources Conservation Service, developed methods to reproduce and update the annual yield maps for chapter 7 of the North Dakota hydrology manual. Annual yields, in acre-feet per square mile, for the 50- and 80-percent exceedance probabilities and expected percentage of snowmelt runoff isolines were estimated using U.S. Geological Survey streamflow data from 1931 to 2016 for 71 selected streamgages with drainage areas of 505 square miles or less. An application of a modified Maintenance of Variance Extension Type III was used to estimate missing annual streamflow volumes. An alternate expected percentage of snowmelt runoff isolines was estimated using High Plains Climatic Center precipitation and snowmelt data from 1931 to 2016 for 85 selected sites. The final expected percentage of snowmelt runoff isolines was estimated using streamflow data instead of precipitation and snowfall depth data. A snowmelt runoff seasonal period of March–May produced better isoline slopes than a November–May runoff seasonal period. Slopes of the expected percentage of snowmelt runoff isolines were sensitive to amounts of missing record. Suitable isoline slopes appeared when the missing record was set to 50 percent (43 years) and 66 percent (57 years) for the 86-year period of 1931–2016.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195144","collaboration":"Prepared in cooperation with the Natural Resources Conservation Service—North Dakota","usgsCitation":"Williams-Sether, T., and Wheeling, S.L., 2020, Small basin annual yield and percentage of snowmelt runoff in North Dakota, 1931–2016: U.S. Geological Survey Scientific Investigations Report 2019–5144, 37 p., https://doi.org/10.3133/sir20195144.","productDescription":"Report: vii, 38 p.; Dataset; 2 Appendixes","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-104356","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":375620,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144.pdf","text":"Report","size":"6.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5144"},{"id":375416,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5144/coverthb.jpg"},{"id":375418,"rank":2,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144_appendix_1.xlsx","text":"Appendix 1","size":"136 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2019–5144 Appendix 1","linkHelpText":"—Table 1.1. Example data and computations for U.S. Geological Survey station 05056100"},{"id":375419,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5144/sir20195144_appendix_2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2019–5144 Appendix 2","linkHelpText":"—R Code Script and Supporting Data for the Modified Maintenance of Variance Extension Type III, MOVE.3, Application"},{"id":375420,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System","description":"USGS Data Release","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"North 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Dakota\",\"nation\":\"USA \"}}]}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503–1608
Mountain View Road
Rapid City, SD 57702

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-06-17","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Williams-Sether, Tara 0000-0001-6515-9416","orcid":"https://orcid.org/0000-0001-6515-9416","contributorId":214143,"corporation":false,"usgs":true,"family":"Williams-Sether","given":"Tara","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780663,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wheeling, Spencer L. 0000-0003-4411-6526","orcid":"https://orcid.org/0000-0003-4411-6526","contributorId":221899,"corporation":false,"usgs":true,"family":"Wheeling","given":"Spencer","email":"","middleInitial":"L.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780664,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227644,"text":"70227644 - 2020 - Geochemical modeling of iron and aluminum precipitation during mixing and neutralization of acid mine drainage","interactions":[],"lastModifiedDate":"2022-01-24T13:13:01.142534","indexId":"70227644","displayToPublicDate":"2020-06-17T07:09:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Geochemical modeling of iron and aluminum precipitation during mixing and neutralization of acid mine drainage","docAbstract":"
Geochemical modeling of precipitation reactions in the complex matrix of acid mine drainage is fundamental to understanding natural attenuation, lime treatment, and treatment procedures that separate constituents for potential reuse or recycling. The three main dissolved constituents in acid mine drainage are iron, aluminum, and sulfate. During the neutralization of acid mine drainage (AMD) by mixing with clean tributaries or by titration with a base such as sodium hydroxide or slaked lime, Ca(OH)2, iron precipitates at pH values of 2–3 if oxidized and aluminum precipitates at pH values of 4–5 and both processes buffer the pH during precipitation. Mixing processes were simulated using the ion-association model in the PHREEQC code. The results are sensitive to the solubility product constant (Ksp) used for the precipitating phases. A field example with data on discharge and water composition of AMD before and after mixing along with massive precipitation of an aluminum phase is simulated and shows that there is an optimal Ksp to give the best fit to the measured data. Best fit is defined when the predicted water composition after mixing and precipitation matches most closely the measured water chemistry. Slight adjustment to the proportion of stream discharges does not give a better fit.
","language":"English","publisher":"MDPI","doi":"10.3390/min10060547","usgsCitation":"Nordstrom, D.K., 2020, Geochemical modeling of iron and aluminum precipitation during mixing and neutralization of acid mine drainage: Minerals, v. 10, no. 6, 547, 12 p., https://doi.org/10.3390/min10060547.","productDescription":"547, 12 p.","ipdsId":"IP-119702","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":456374,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min10060547","text":"Publisher Index Page"},{"id":394754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Alpine County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-119.9025,38.9332],[-119.8471,38.8948],[-119.8261,38.8802],[-119.8223,38.8775],[-119.7899,38.8554],[-119.7873,38.8541],[-119.752,38.8297],[-119.743,38.8235],[-119.7285,38.8132],[-119.7033,38.7953],[-119.678,38.7781],[-119.6166,38.7357],[-119.6088,38.7303],[-119.5977,38.7226],[-119.5771,38.7084],[-119.5847,38.7029],[-119.5883,38.698],[-119.5912,38.6907],[-119.5965,38.6762],[-119.6024,38.6703],[-119.6118,38.668],[-119.6159,38.6649],[-119.6159,38.6585],[-119.6159,38.6458],[-119.6165,38.6345],[-119.6194,38.6254],[-119.6223,38.6159],[-119.6188,38.6059],[-119.6129,38.5991],[-119.5987,38.5924],[-119.597,38.5919],[-119.5958,38.5842],[-119.5928,38.5769],[-119.5893,38.5706],[-119.5869,38.5674],[-119.5857,38.5552],[-119.5781,38.5484],[-119.5734,38.5452],[-119.5687,38.5421],[-119.5657,38.5298],[-119.5592,38.5239],[-119.5551,38.5162],[-119.5592,38.5103],[-119.5586,38.5049],[-119.5557,38.5026],[-119.5498,38.5022],[-119.5427,38.4986],[-119.5451,38.4895],[-119.5427,38.4832],[-119.5463,38.4759],[-119.5527,38.4718],[-119.5574,38.4682],[-119.558,38.4591],[-119.5603,38.4446],[-119.5627,38.4378],[-119.5692,38.4356],[-119.5674,38.4283],[-119.5662,38.4247],[-119.5644,38.4215],[-119.5621,38.4165],[-119.5627,38.4093],[-119.5674,38.4056],[-119.5762,38.4034],[-119.582,38.4007],[-119.5856,38.397],[-119.5991,38.4047],[-119.6026,38.4038],[-119.6061,38.4011],[-119.6132,38.4015],[-119.6214,38.3961],[-119.6208,38.3911],[-119.6125,38.373],[-119.6078,38.3698],[-119.6084,38.3671],[-119.6149,38.3662],[-119.6248,38.3594],[-119.6342,38.3544],[-119.6301,38.3476],[-119.633,38.3421],[-119.6313,38.3372],[-119.6348,38.3308],[-119.6354,38.3294],[-119.6365,38.3286],[-119.6594,38.3461],[-119.6986,38.3652],[-119.6924,38.3791],[-119.6967,38.4089],[-119.708,38.4177],[-119.751,38.4168],[-119.7686,38.4067],[-119.7785,38.4078],[-119.7836,38.4051],[-119.8006,38.4015],[-119.8133,38.3877],[-119.8362,38.3826],[-119.8529,38.3728],[-119.8689,38.3678],[-119.8793,38.3597],[-120.0169,38.4363],[-120.0528,38.4534],[-120.0727,38.4478],[-120.0719,38.4936],[-120.072,38.5013],[-120.0727,38.5117],[-120.0719,38.5684],[-120.0729,38.611],[-120.0732,38.6554],[-120.0741,38.7016],[-119.9685,38.7746],[-119.9567,38.7796],[-119.9479,38.7842],[-119.9444,38.7887],[-119.942,38.7919],[-119.9391,38.7947],[-119.9439,38.8028],[-119.9368,38.8096],[-119.9292,38.8183],[-119.9215,38.8233],[-119.9239,38.8283],[-119.9216,38.8323],[-119.911,38.8342],[-119.908,38.8365],[-119.9074,38.8383],[-119.9075,38.8469],[-119.9052,38.8569],[-119.8969,38.8564],[-119.8904,38.8578],[-119.8833,38.8637],[-119.8792,38.866],[-119.8775,38.8692],[-119.8775,38.8724],[-119.8781,38.8746],[-119.887,38.8805],[-119.8799,38.8855],[-119.8841,38.8964],[-119.8806,38.9032],[-119.8854,38.9145],[-119.8985,38.9226],[-119.9003,38.9262],[-119.9025,38.9332]]]},\"properties\":{\"name\":\"Alpine\",\"state\":\"CA\"}}]}","volume":"10","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Nordstrom, D. Kirk 0000-0003-3283-5136 dkn@usgs.gov","orcid":"https://orcid.org/0000-0003-3283-5136","contributorId":749,"corporation":false,"usgs":true,"family":"Nordstrom","given":"D.","email":"dkn@usgs.gov","middleInitial":"Kirk","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":false,"id":831503,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70210673,"text":"sir20205040 - 2020 - Missouri StreamStats—St. Louis County and the City of St. Louis urban application","interactions":[],"lastModifiedDate":"2020-06-16T20:33:11.424554","indexId":"sir20205040","displayToPublicDate":"2020-06-16T09:37:03","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5040","displayTitle":"Missouri StreamStats—St. Louis County and the City of St. Louis Urban Application","title":"Missouri StreamStats—St. Louis County and the City of St. Louis urban application","docAbstract":"

To address a major limitation of the functionality of the Missouri statewide StreamStats application in the urban areas of St. Louis County and the City of St. Louis, Missouri, the U.S. Geological Survey, in cooperation with the Metropolitan St. Louis Sewer District, defined watershed boundaries and hydrography for the study area using high-resolution 3-meter digital elevation data derived from light detection and ranging sources, high-resolution 6-inch imagery, and storm sewer network geospatial data. The combined sanitary sewers, a part of the storm sewer network, were integrated into the open channel hydrography and elevation data using a new Arc Hydro stormwater tool developed to facilitate the incorporation of the combined sanitary sewer network into the StreamStats application.

The combined sanitary sewer network was edited for connectivity and flow direction before integration into the Missouri-St. Louis StreamStats application. Inlet structures in the geospatial data were defined as HydroJunction features that allow for stormwater runoff to enter the combined sanitary sewer network. An Arc Hydro stormwater processing workflow and a sewershed delineation tool were developed to integrate the combined sanitary sewer network with the hydrographic dataset and digital elevation model in the study area.

The StreamStats application developed for the study area provides various data exploration tools that can be used to examine the spatial data and to obtain general descriptive information and flow statistics at streamgages in the study area. Watersheds and sewersheds can be delineated and basin characteristics can be determined at any point on the open channel network or the combined sanitary sewer network in the study area. Peak-flow statistics can be computed at any point on the open channel network. A report summarizing the results is generated by the StreamStats application and can be downloaded and used in other software.

The Missouri-St. Louis StreamStats application is limited to the area inside St. Louis County and the City of St. Louis and excludes locations on the main stem of the Mississippi, Missouri, and Meramec Rivers. The limitations of the Missouri-St. Louis StreamStats application include possible inaccuracies using regression equations for peak-flow statistics developed assuming natural flow conditions and topographically derived watersheds determined from a coarser resolution of data than is used in this application. Additionally, published regression equations for peak-flow statistics did not incorporate any pipe flow or sewershed delineations when they were developed, which limits the applicability of peak-flow statistics to basins based on primarily topographic delineation. Inaccuracies in resolution, completeness, location, or attribution of geospatial elevation data, hydrographic data, derived stream lines, derived watershed boundaries, and combined sanitary sewer data can limit the accuracy and functionality of the Missouri-St. Louis StreamStats application.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205040","collaboration":"Prepared in cooperation with the Metropolitan St. Louis Sewer District","usgsCitation":"Southard, R.E., Haluska, T., Richards, J.M., Ellis, J.T., Dartiguenave, C., and Djokic, D., 2020, Missouri StreamStats—St. Louis County and the City of St. Louis urban application: U.S. Geological Survey Scientific Investigations Report 2020–5040, 27 p., https://doi.org/10.3133/sir20205040.","productDescription":"Report: vii, 27 p.; Appendix; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-098907","costCenters":[{"id":36532,"text":"Central Midwest Water Science 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Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2020-06-16","noUsgsAuthors":false,"publicationDate":"2020-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Southard, Rodney E. 0000-0001-8024-9698 southard@usgs.gov","orcid":"https://orcid.org/0000-0001-8024-9698","contributorId":3880,"corporation":false,"usgs":true,"family":"Southard","given":"Rodney","email":"southard@usgs.gov","middleInitial":"E.","affiliations":[],"preferred":true,"id":790897,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haluska, Tana 0000-0001-6307-4769 thaluska@usgs.gov","orcid":"https://orcid.org/0000-0001-6307-4769","contributorId":1708,"corporation":false,"usgs":true,"family":"Haluska","given":"Tana","email":"thaluska@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790898,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Richards, Joseph M. 0000-0002-9822-2706 richards@usgs.gov","orcid":"https://orcid.org/0000-0002-9822-2706","contributorId":2370,"corporation":false,"usgs":true,"family":"Richards","given":"Joseph","email":"richards@usgs.gov","middleInitial":"M.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790899,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ellis, Jarrett T. 0000-0001-9928-1030","orcid":"https://orcid.org/0000-0001-9928-1030","contributorId":210378,"corporation":false,"usgs":true,"family":"Ellis","given":"Jarrett","email":"","middleInitial":"T.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790900,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dartiguenave, Christine","contributorId":225362,"corporation":false,"usgs":false,"family":"Dartiguenave","given":"Christine","email":"","affiliations":[],"preferred":false,"id":790901,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Djokic, Dean","contributorId":12912,"corporation":false,"usgs":true,"family":"Djokic","given":"Dean","email":"","affiliations":[],"preferred":false,"id":790902,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70198567,"text":"sir20185104 - 2020 - Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources","interactions":[],"lastModifiedDate":"2020-06-16T14:09:59.229883","indexId":"sir20185104","displayToPublicDate":"2020-06-16T09:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5104","displayTitle":"Conceptual Framework and Approach for Conducting a Geoenvironmental Assessment of Undiscovered Uranium Resources","title":"Conceptual framework and approach for conducting a geoenvironmental assessment of undiscovered uranium resources","docAbstract":"

This report presents a novel conceptual framework and approach for conducting a geologically based environmental assessment, or geoenvironmental assessment, of undiscovered uranium resources within an area likely to contain uranium deposits. The framework is based on a source-to-receptor model that prioritizes the most likely contaminant sources, contaminant pathways, and affected environmental media for three common uranium extraction methods—open pit or underground mining with milling and in situ recovery (ISR). Data on regional geology, hydrology, and climate, as well as historical uranium mining and milling records are used to estimate the probable amounts of waste rock, tailings, wastewater, surface land disturbance, and subsurface aquifer disturbance for likely mining methods. Constituents of concern that might take the form of leachates, dust, radon, and sediments formed by chemical and physical weathering are also identified in the geoenvironmental assessment. Finally, areas where constituents of concern are likely to occur and persist in air, land, surface water, and groundwater are indicated by the potential for dispersion of dust by wind, accumulation of radon because of air stagnation, dispersion of sediments and wastewater by runoff, and infiltration of wastewater or leachates with consideration of the likely mobility of contaminants in surface water and groundwater. The geoenvironmental assessment output can be summarized in the following primary products: (1) a descriptive geoenvironmental model; (2) maps and statistics of variables that indicate the potential for constituents of concern to occur and persist in air, land, surface water, and groundwater within a tract that is geologically permissive for the occurrence of uranium; and (3) tables providing estimated or indicated quantities of waste rock, tailings, wastewater, dust, and radon emissions that could be associated with undiscovered uranium resources, if extracted, for each permissive tract. The uranium geoenvironmental assessment could help natural resource managers to prioritize and (or) identify (1) important potential contaminant pathways, (2) management practices required depending on the types of constituents that could be of concern, (3) areas for response in the event of accidental release, and (4) future directions for study. Furthermore, indicators of rock and water volumes potentially associated with an undiscovered uranium deposit may be evaluated to make quantitative comparisons of water required for uranium production or potential waste products generated during uranium extraction from areas permissive for uranium resource occurrence throughout the United States.

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Eastern Energy Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-06-16","noUsgsAuthors":false,"publicationDate":"2020-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":741953,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walton-Day, Katherine 0000-0002-5908-2683 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-5908-2683","contributorId":206860,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":741954,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seal,, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":141204,"corporation":false,"usgs":true,"family":"Seal,","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":741955,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236094,"text":"70236094 - 2020 - Kinematic rupture and 3D wave propagation simulations of the 2019 Mw 7.1 Ridgecrest, California, earthquake","interactions":[],"lastModifiedDate":"2022-08-29T11:59:56.732315","indexId":"70236094","displayToPublicDate":"2020-06-16T06:57:02","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Kinematic rupture and 3D wave propagation simulations of the 2019 Mw 7.1 Ridgecrest, California, earthquake","docAbstract":"

We model the kinematic rupture process of the 2019 Mw\">MwMw 7.1 Ridgecrest, California, earthquake using numerical simulations to reproduce the elastodynamic wave field observed by inertial seismometers, high‐rate Global Navigation Satellite System stations, and borehole strainmeters. This was the largest earthquake in Southern California in 20 yr and was widely felt throughout the region. The Mw\">MwMw 7.1 mainshock was part of a large sequence of &#x223C;30,000\">∼30,000∼30,000 aftershocks and was notably preceded by an Mw\">MwMw 6.4 foreshock by 34 hr on fault structures that were once poorly understood. A large number of seismic and geodetic instruments measured the rupture process for both events, with many stations located in the near field. Hence, this is a rare opportunity to better understand complex earthquake processes that arise in an immature fault zone using advanced computing. Of the kinematic rupture models that we tested, our preferred is the simplest one that reproduces signals recorded by the three different geophysical datasets; it is composed of four distinct ruptures that progressively migrate to the southeast with delayed initiation times, and typical rupture speeds. This type of model does a better job at matching the recorded ground motions and deformations than does one composed of a continuous rupture with very low‐rupture velocity, as proposed in other studies of this earthquake.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200031","usgsCitation":"Hirakawa, E.T., and Barbour, A.J., 2020, Kinematic rupture and 3D wave propagation simulations of the 2019 Mw 7.1 Ridgecrest, California, earthquake: Bulletin of the Seismological Society of America, v. 110, no. 4, p. 1644-1659, https://doi.org/10.1785/0120200031.","productDescription":"16 p.","startPage":"1644","endPage":"1659","ipdsId":"IP-115108","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":405783,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Ridgecrest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.98217773437499,\n 35.38904996691167\n ],\n [\n -117.3614501953125,\n 35.38904996691167\n ],\n [\n -117.3614501953125,\n 35.79108281624994\n ],\n [\n -117.98217773437499,\n 35.79108281624994\n ],\n [\n -117.98217773437499,\n 35.38904996691167\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"110","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Hirakawa, Evan Tyler 0000-0002-5720-0850","orcid":"https://orcid.org/0000-0002-5720-0850","contributorId":295776,"corporation":false,"usgs":true,"family":"Hirakawa","given":"Evan","email":"","middleInitial":"Tyler","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":849974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barbour, Andrew J. 0000-0002-6890-2452","orcid":"https://orcid.org/0000-0002-6890-2452","contributorId":215339,"corporation":false,"usgs":true,"family":"Barbour","given":"Andrew","middleInitial":"J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":849975,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228523,"text":"70228523 - 2020 - Keeping it classy: Classification of live fish and ghost PIT tags detected with a mobile PIT tag interrogation system using an innovative analytical approach","interactions":[],"lastModifiedDate":"2022-02-11T12:48:31.240069","indexId":"70228523","displayToPublicDate":"2020-06-16T06:44:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Keeping it classy: Classification of live fish and ghost PIT tags detected with a mobile PIT tag interrogation system using an innovative analytical approach","docAbstract":"
The ability of passive integrated transponder (PIT) tag data to improve demographic parameter estimates has led to the rapid advancement of PIT tag systems. However, ghost tags create uncertainty about detected tag status (i.e., live fish or ghost tag) when using mobile interrogation systems. We developed a method to differentiate between live fish and ghost tags using a random forest classification model with a novel data input structure based on known fate PIT tag detections in the San Juan River (New Mexico, Colorado, and Utah, USA). We used our model to classify detected tags with an overall error rate of 6.8% (1.6% ghost tags error rate and 21.8% live fish error rate). The important variables for classification were related to distance moved and response to monsoonal flood flows; however, habitat variables did not appear to influence model accuracy. Our results and approach allow the use of mobile detection data with confidence and allow for greater accuracy in movement, distribution, and habitat use studies, potentially helping identify influential management actions that would improve our ability to conserve and recover endangered fish.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0403","usgsCitation":"Stout, J.B., Conner, M., Budy, P., Mackinnon, P., and McKinstry, M., 2020, Keeping it classy: Classification of live fish and ghost PIT tags detected with a mobile PIT tag interrogation system using an innovative analytical approach: Canadian Journal of Fisheries and Aquatic Sciences, v. 7, no. 9, p. 1564-1573, https://doi.org/10.1139/cjfas-2019-0403.","productDescription":"10 p.","startPage":"1564","endPage":"1573","ipdsId":"IP-116366","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":456380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2019-0403","text":"Publisher Index Page"},{"id":395838,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stout, J. Benjamin","contributorId":276038,"corporation":false,"usgs":false,"family":"Stout","given":"J.","email":"","middleInitial":"Benjamin","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":834499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conner, Mary","contributorId":276039,"corporation":false,"usgs":false,"family":"Conner","given":"Mary","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":834500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Budy, Phaedra E. 0000-0002-9918-1678","orcid":"https://orcid.org/0000-0002-9918-1678","contributorId":228930,"corporation":false,"usgs":true,"family":"Budy","given":"Phaedra E.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834498,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mackinnon, Peter","contributorId":276040,"corporation":false,"usgs":false,"family":"Mackinnon","given":"Peter","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":834501,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McKinstry, Mark","contributorId":276041,"corporation":false,"usgs":false,"family":"McKinstry","given":"Mark","email":"","affiliations":[{"id":12646,"text":"BOR","active":true,"usgs":false}],"preferred":false,"id":834502,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210631,"text":"sir20205036 - 2020 - Updating data inputs, assessing trends, and evaluating a method to estimate probable high groundwater levels in selected areas of Massachusetts","interactions":[],"lastModifiedDate":"2020-06-15T15:30:35.833622","indexId":"sir20205036","displayToPublicDate":"2020-06-15T10:50:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5036","displayTitle":"Updating Data Inputs, Assessing Trends, and Evaluating a Method To Estimate Probable High Groundwater Levels in Selected Areas of Massachusetts","title":"Updating data inputs, assessing trends, and evaluating a method to estimate probable high groundwater levels in selected areas of Massachusetts","docAbstract":"

A method to estimate the probable high groundwater level in Massachusetts, excluding Cape Cod and the islands, was developed in 1981. The method uses a groundwater measurement from a test site, groundwater measurements from an index well, and a distribution of high groundwater levels from wells in similar geologic and topographic settings. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, conducted an update to the Frimpter method for estimating the probable high groundwater levels in Massachusetts. The study evaluated the potential changes to the method resulting from four decades of additional groundwater-level data and the expansion of the network of wells for monitoring groundwater levels. The differences and potential benefits of daily, as opposed to monthly, measurements in the application of the method were examined because of the increased availability of high-frequency (subdaily) groundwater-level data. The study also considered long-term trends in groundwater levels that may alter the accuracy of the method. Finally, the accuracy of the estimated high groundwater levels was evaluated, and improved implementation guidance was prepared.

For this study, groundwater levels in 153 wells in Massachusetts and surrounding States with records with lengths of 16 to 78 years were analyzed. The highest recorded groundwater levels ranged from 1.2 feet (ft) above land surface (flooded conditions) to 45.8 ft below land surface, with a median of 4.6 ft below land surface. The maximum annual groundwater-level range was 1.4 to 17.9 ft, with a median of 5.5 ft.

The within-month variation, maximum annual groundwater-level range, and highest recorded groundwater level were computed using daily mean groundwater-level values from 28 wells with continuous records. The use of daily data resulted in larger maximum annual groundwater-level ranges (0.02 to 2.94 ft larger, with a median of 0.58 ft larger) and shallower highest-recorded groundwater levels (0.0 to 1.60 ft shallower, with a median of 0.18 ft shallower) than computations based on monthly measurements in the same wells.

Statistical tests showed moderate to strong evidence of trends in measurements of both high and low groundwater levels within most of the periods during which water levels were analyzed. High groundwater levels rose beneath the land surface at most sites during four of the six periods used for analysis (1966–2015, 1986–2015, 1991–2010, and 1981–2010). Low groundwater levels also increased at many sites during most of the periods evaluated, but this trend was less widespread than the similar trends in high groundwater levels, and the trend was to deeper low groundwater levels at more sites than the trend to deeper high groundwater levels. There was no clear trend in annual groundwater-level ranges at most sites during the six periods analyzed.

In general, the Frimpter method predicted shallower (higher) high groundwater levels than were observed but correctly classified sites according to their suitabilities for unmounded septic systems. The mean error of the predictions (difference between the estimated and observed groundwater levels) ranged from −3.23 ft to −1.40 ft for various approaches to estimating the groundwater-level range and selecting an index well. The method correctly classified 83 to 86 percent of monitoring-well sites according to their suitability for an unmounded septic system for many approaches to estimating the annual groundwater-level range and selecting an index well. The approach selected for estimating the annual groundwater-level range and selecting an index well will depend upon the importance of an accurate estimate of the high groundwater level as compared to the importance of an estimated high groundwater level that is less likely to be exceeded.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205036","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Barclay, J.R., and Mullaney, J.R., 2020, Updating data inputs, assessing trends, and evaluating a method to estimate probable high groundwater levels in selected areas of Massachusetts: U.S. Geological Survey Scientific Investigations Report 2020–5036, 45 p., https://doi.org/10.3133/sir20205036.","productDescription":"Report: viii, 45 p.; Data Release","numberOfPages":"45","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103689","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":375551,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NM2PHP","text":"USGS data release","linkHelpText":"Data on well characteristics and well-pair characteristics for estimating high groundwater levels in selected areas of Massachusetts"},{"id":375553,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5036/sir20205036.pdf","text":"Report","size":"7.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5036"},{"id":375554,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5036/coverthb2.jpg"}],"country":"United States","state":"Connecticut, Massachusetts, New Hampshire, Rhode Island, Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.8343505859375,\n 42.90011265525328\n ],\n [\n -73.2952880859375,\n 42.9524020856897\n ],\n [\n -73.27880859375,\n 42.65820178455667\n ],\n [\n -73.5150146484375,\n 42.12267315117256\n ],\n [\n -73.5479736328125,\n 41.393294288784865\n ],\n [\n -73.54248046875,\n 41.29431726315258\n ],\n [\n -73.487548828125,\n 41.20345619205131\n ],\n [\n -73.7347412109375,\n 41.10005163093046\n ],\n [\n -73.65234375,\n 41.000629848685385\n ],\n [\n -72.9547119140625,\n 41.14143302653628\n ],\n [\n -72.0538330078125,\n 41.17451935556443\n ],\n [\n -71.43310546875,\n 41.29431726315258\n ],\n [\n -70.6475830078125,\n 41.21585377825921\n ],\n [\n -69.7686767578125,\n 41.16211393939692\n ],\n [\n -69.8785400390625,\n 41.87774145109676\n ],\n [\n -70.1806640625,\n 42.17968819665961\n ],\n [\n -70.57617187499999,\n 42.718768102606326\n ],\n [\n -70.8343505859375,\n 42.90011265525328\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2020-06-15","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Barclay, Janet R. 0000-0003-1643-6901 jbarclay@usgs.gov","orcid":"https://orcid.org/0000-0003-1643-6901","contributorId":222437,"corporation":false,"usgs":true,"family":"Barclay","given":"Janet","email":"jbarclay@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790894,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullaney, John R. 0000-0003-4936-5046 jmullane@usgs.gov","orcid":"https://orcid.org/0000-0003-4936-5046","contributorId":1957,"corporation":false,"usgs":true,"family":"Mullaney","given":"John","email":"jmullane@usgs.gov","middleInitial":"R.","affiliations":[{"id":196,"text":"Connecticut Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":790895,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210700,"text":"70210700 - 2020 - Snow processes in mountain forests: Interception modeling for coarse-scale applications","interactions":[],"lastModifiedDate":"2020-06-18T14:54:10.16543","indexId":"70210700","displayToPublicDate":"2020-06-15T09:50:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Snow processes in mountain forests: Interception modeling for coarse-scale applications","docAbstract":"

Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.

","language":"English","doi":"10.5194/hess-24-2545-2020","usgsCitation":"Helbig, N., Moeser, C.D., Teich, M., Vincent, L., Lejeune, Y., Sicart, J., and Monnet, J., 2020, Snow processes in mountain forests: Interception modeling for coarse-scale applications: Hydrology and Earth System Sciences, v. 24, p. 2545-2560, https://doi.org/10.5194/hess-24-2545-2020.","productDescription":"16 p.","startPage":"2545","endPage":"2560","ipdsId":"IP-111174","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":456397,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-24-2545-2020","text":"Publisher Index Page"},{"id":375684,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"France, United States","state":"Utah","otherGeospatial":"French Alps, Rocky Mountains","volume":"24","noUsgsAuthors":false,"publicationDate":"2020-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Helbig, N. 0000-0002-8663-7306","orcid":"https://orcid.org/0000-0002-8663-7306","contributorId":225392,"corporation":false,"usgs":false,"family":"Helbig","given":"N.","email":"","affiliations":[{"id":41093,"text":"WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland","active":true,"usgs":false}],"preferred":false,"id":791020,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moeser, C. David 0000-0003-0154-9110","orcid":"https://orcid.org/0000-0003-0154-9110","contributorId":214563,"corporation":false,"usgs":true,"family":"Moeser","given":"C.","email":"","middleInitial":"David","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791021,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teich, M. 0000-0002-8850-9279","orcid":"https://orcid.org/0000-0002-8850-9279","contributorId":225393,"corporation":false,"usgs":false,"family":"Teich","given":"M.","email":"","affiliations":[{"id":41094,"text":"Austrian Research Centre for Forests (BFW), Innsbruck, Austria","active":true,"usgs":false}],"preferred":false,"id":791022,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vincent, L.","contributorId":225394,"corporation":false,"usgs":false,"family":"Vincent","given":"L.","email":"","affiliations":[{"id":41095,"text":"University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791023,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lejeune, Y.","contributorId":225395,"corporation":false,"usgs":false,"family":"Lejeune","given":"Y.","email":"","affiliations":[{"id":41095,"text":"University Grenoble Alpes, University Toulouse, Météo-France, CNRS, CNRM, Centre d’Etudes de la Neige, Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791024,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sicart, J.-E.","contributorId":225396,"corporation":false,"usgs":false,"family":"Sicart","given":"J.-E.","email":"","affiliations":[{"id":41096,"text":"Université Grenoble Alpes, CNRS, IRD, Grenoble INP, Institut des Géosciences de l’Environnement (IGE) - UMR 5001,","active":true,"usgs":false}],"preferred":false,"id":791025,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monnet, J.-M.","contributorId":225397,"corporation":false,"usgs":false,"family":"Monnet","given":"J.-M.","email":"","affiliations":[{"id":41097,"text":"Univ. Grenoble Alpes, Irstea, LESSEM, 38000 Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":791026,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70210859,"text":"70210859 - 2020 - Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States","interactions":[],"lastModifiedDate":"2020-06-30T13:29:12.764279","indexId":"70210859","displayToPublicDate":"2020-06-15T08:24:57","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":"Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States","docAbstract":"The National Park Service (NPS) manages hundreds of parks in the United States, and many contain important aquatic ecosystems and/or threatened and endangered aquatic species vulnerable to hydro-climatic change. Effective management of park resources under future hydro-climatic uncertainty requires information on both baseline conditions and the range of projected future conditions. A monthly water balance model was used to assess baseline (1981-1999) conditions and a range of projected future hydro-climatic conditions in 374 NPS parks. General circulation model outputs representing 214 future climate simulations were used to drive the model. Projected future changes in temperature (T), precipitation (P), and runoff (R) are expressed as departures from historical baselines. Climate simulations indicate increasing T in 2030 for all parks with 50th percentile simulations projecting increases of 1.67 oC or more in 50% of parks. Departures in 2030 P indicate a mix of mostly increases and some decreases, with 50th percentile simulations projecting increases in P in more than 70% of parks. Departures in R for 2030 are mostly decreases , with the 50th percentile simulations projecting decreases in R in more than 50% of parks in all seasons except winter. Hence in many parks, R is projected to decrease even when P is projected to increase because of increasing T in all NPS parks. Projected changes in future hydro-climatic conditions can also be assessed for individual parks, and Rocky Mountain National Park and Congaree National Park are used as examples.","language":"English","publisher":"MDPI","doi":"10.3390/w12061704","usgsCitation":"Battaglin, W., Hay, L., Lawrence, D.J., McCabe, G.J., and Norton, P.A., 2020, Baseline conditions and projected future hydro-climatic change in National Parks in the conterminous United States: Water, v. 6, no. 12, 1704, 24 p., https://doi.org/10.3390/w12061704.","productDescription":"1704, 24 p.","ipdsId":"IP-117255","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":456399,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12061704","text":"Publisher Index Page"},{"id":376013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n 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Center","active":true,"usgs":true}],"preferred":true,"id":791750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hay, Lauren","contributorId":209524,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","affiliations":[],"preferred":true,"id":791751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, David J.","contributorId":34374,"corporation":false,"usgs":true,"family":"Lawrence","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":791752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":791753,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Norton, Parker A. 0000-0002-4638-2601 pnorton@usgs.gov","orcid":"https://orcid.org/0000-0002-4638-2601","contributorId":2257,"corporation":false,"usgs":true,"family":"Norton","given":"Parker","email":"pnorton@usgs.gov","middleInitial":"A.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":791754,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211015,"text":"70211015 - 2020 - Conceptualizing alternate regimes in a large floodplain-river ecosystem","interactions":[],"lastModifiedDate":"2020-07-10T13:20:01.000249","indexId":"70211015","displayToPublicDate":"2020-06-15T08:16:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Conceptualizing alternate regimes in a large floodplain-river ecosystem","docAbstract":"Regime shifts –persistent changes in the structure and function of an ecosystem - are well-documented in many ecosystems but remain poorly understood in floodplain-river ecosystems. We apply a resilience perspective to large floodplain-river ecosystems by presenting three examples of plausible sets of alternate regimes that are relevant to natural resource management interests within the Upper Mississippi River and Illinois River. These alternate regimes include: 1) a clear water and abundant vegetation regime vs. a turbid water and sparse vegetation regime in lentic, off-channel areas, 2) a diverse native fish community regime vs. an invasive-dominated fish community regime, and 3) a regime characterized by a diverse and dynamic mosaic of floodplain vegetation types vs. one characterized as a persistent invasive wet meadow monoculture. For each set of potential alternate regimes, we synthesize known or hypothesized feedback mechanisms that reinforce regimes, controlling variables that drive regime transitions, and restoration pathways. The conceptual models presented here provide a framework for synthesizing our understanding of the dynamics of this ecosystem and are relevant to other large floodplain-river ecosystems that face similar human pressures across the world. The models are currently being used to prioritize future research, test hypotheses, and inform restoration and management on the Upper Mississippi River and Illinois River. Through sharing our approach, we provide a case study in which we document an important step in operationalizing resilience concepts for the management of natural resources.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2020.110516","usgsCitation":"Bouska, K.L., Houser, J.N., De Jager, N.R., Drake, D.C., Collins, S.F., Gibson-Reniemer, C.K., and Thomsen, M.A., 2020, Conceptualizing alternate regimes in a large floodplain-river ecosystem: Journal of Environmental Management, v. 264, 110516, 15 p., https://doi.org/10.1016/j.jenvman.2020.110516.","productDescription":"110516, 15 p.","ipdsId":"IP-108847","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":376247,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin, Iowa, Illinois, Missouri","otherGeospatial":"Upper Mississippi River, Illinois River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.58154296875,\n 37.020098201368114\n ],\n [\n -88.22021484375,\n 37.020098201368114\n ],\n [\n -88.22021484375,\n 45.27488643704891\n ],\n [\n -93.58154296875,\n 45.27488643704891\n ],\n [\n -93.58154296875,\n 37.020098201368114\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"264","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bouska, Kristen L. 0000-0002-4115-2313 kbouska@usgs.gov","orcid":"https://orcid.org/0000-0002-4115-2313","contributorId":178005,"corporation":false,"usgs":true,"family":"Bouska","given":"Kristen","email":"kbouska@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Houser, Jeffrey N. 0000-0003-3295-3132 jhouser@usgs.gov","orcid":"https://orcid.org/0000-0003-3295-3132","contributorId":2769,"corporation":false,"usgs":true,"family":"Houser","given":"Jeffrey","email":"jhouser@usgs.gov","middleInitial":"N.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":792432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Drake, Deanne C.","contributorId":207846,"corporation":false,"usgs":false,"family":"Drake","given":"Deanne","email":"","middleInitial":"C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":792433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Collins, Scott F.","contributorId":172292,"corporation":false,"usgs":false,"family":"Collins","given":"Scott","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":792434,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gibson-Reniemer, Caniel K.","contributorId":228874,"corporation":false,"usgs":false,"family":"Gibson-Reniemer","given":"Caniel","email":"","middleInitial":"K.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":792435,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thomsen, Meredith A.","contributorId":228875,"corporation":false,"usgs":false,"family":"Thomsen","given":"Meredith","email":"","middleInitial":"A.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":792436,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70225148,"text":"70225148 - 2020 - Using a bayesian multistate occupancy model to assess seabird and shorebird status in Glacier Bay, Alaska","interactions":[],"lastModifiedDate":"2021-10-14T12:44:11.60355","indexId":"70225148","displayToPublicDate":"2020-06-15T07:41:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Using a bayesian multistate occupancy model to assess seabird and shorebird status in Glacier Bay, Alaska","docAbstract":"

The U.S. Department of Interior National Park Service is charged with both monitoring avian communities and evaluating the influence of visitors to National Parks on sensitive species; however, this task is challenging considering that sampling programs often involve multiple species, each with differing behavior, habitat requirements, and detectability. Our objectives were to build a model to describe the status of waterbirds in Glacier Bay National Park, Alaska, USA, and assess effects of area closures on these species. We used a Bayesian multistate occupancy model to describe the status of multiple species and make the best possible use of existing survey data. We modeled up to 5 states per species and evaluated predictors of occupancy, nesting, and abundance, as well as survey-related predictors of state-dependent detection probability. We found that occupancy probability varied across species and habitats (islands vs. glacial outwashes). For most species, occupancy probability was substantially greater at sites occupied in the year previous (site persistence). We found weak evidence that area closures affected the occurrence of species in the study, but this was largely because most sites were closed for the entirety of the study period. The probability of detecting occurrence, nesting, and abundance varied across species and survey methods (ground vs. vessel). Detection parameters provided valuable information for enhancing the efficiency of future surveys, by identifying preferred survey methods and sampling periods for specific waterbird species. © 2020 The Wildlife Society.

","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1100","usgsCitation":"Whitlock, S., Lewis, T., and Peterson, J., 2020, Using a bayesian multistate occupancy model to assess seabird and shorebird status in Glacier Bay, Alaska: Wildlife Society Bulletin, v. 44, no. 3, p. 451-467, https://doi.org/10.1002/wsb.1100.","productDescription":"17 p.","startPage":"451","endPage":"467","ipdsId":"IP-105880","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":390519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Glacier Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -139.63623046875,\n 57.237448817822425\n ],\n [\n -132.16552734375,\n 57.237448817822425\n ],\n [\n -132.16552734375,\n 59.58441353704829\n ],\n [\n -139.63623046875,\n 59.58441353704829\n ],\n [\n -139.63623046875,\n 57.237448817822425\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Whitlock, Steven L.","contributorId":267708,"corporation":false,"usgs":false,"family":"Whitlock","given":"Steven L.","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":825161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lewis, Tania","contributorId":100960,"corporation":false,"usgs":true,"family":"Lewis","given":"Tania","email":"","affiliations":[],"preferred":false,"id":825162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":825160,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215145,"text":"70215145 - 2020 - Mysterious tsunami in the Caribbean Sea following the 2010 Haiti earthquake possibly generated by dynamically triggered early aftershocks","interactions":[],"lastModifiedDate":"2020-10-08T12:23:25.287315","indexId":"70215145","displayToPublicDate":"2020-06-15T07:18:16","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Mysterious tsunami in the Caribbean Sea following the 2010 Haiti earthquake possibly generated by dynamically triggered early aftershocks","docAbstract":"

Dynamically triggered offshore aftershocks, caused by passing seismic waves from main shocks located on land, are currently not considered in tsunami warnings. The M7.0 2010 Haiti earthquake epicenter was located on land 27 km north of the Caribbean Sea and its focal mechanism was oblique strike-slip. Nevertheless, a tsunami recorded on a Caribbean Deep-Ocean Assessment and Reporting of Tsunami (DART) buoy and a tide gauge produced runup heights of 1–3 m along Haiti southeast coast. Earthquake finite-fault model inversions of the DART waveform suggest that a reverse fault doublet with magnitudes of M6.8 and M6.5 located 85 km southwest of the epicenter may have excited the tsunami. This doublet collocates with dynamically triggered aftershocks, derived from back-projection analysis, that occurred 20-60 s after the main shock of the Haiti earthquake. The aftershocks are within a region of maximum dynamic strain predicted by the main shock, on a possibly tectonically active submarine ridge southwest of Haiti's Southern Peninsula. The agreement between the tsunami finite-fault source models and the seismic and tectonic evidence suggests that earthquakes on land, even strike-slip faults, can generate tsunamis by dynamically triggering offshore aftershocks.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.epsl.2020.116269","usgsCitation":"ten Brink, U., Wei, Y., Fan, W., Granja-Bruna, J., and Miller, N.C., 2020, Mysterious tsunami in the Caribbean Sea following the 2010 Haiti earthquake possibly generated by dynamically triggered early aftershocks: Earth and Planetary Science Letters, v. 540, 116269, 11 p., https://doi.org/10.1016/j.epsl.2020.116269.","productDescription":"116269, 11 p.","ipdsId":"IP-092711","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":456403,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.epsl.2020.116269","text":"Publisher Index Page"},{"id":379215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Dominican Republic, Haiti","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.794921875,\n 17.26672782352052\n ],\n [\n -67.862548828125,\n 17.26672782352052\n ],\n [\n -67.862548828125,\n 20.478481600090568\n ],\n [\n -74.794921875,\n 20.478481600090568\n ],\n [\n -74.794921875,\n 17.26672782352052\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"540","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"ten Brink, Uri S. 0000-0001-6858-3001 utenbrink@usgs.gov","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":127560,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri S.","email":"utenbrink@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":800991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wei, Yong","contributorId":242870,"corporation":false,"usgs":false,"family":"Wei","given":"Yong","affiliations":[{"id":48562,"text":"JISAO, University of Washington, WA 98105 USA","active":true,"usgs":false}],"preferred":false,"id":800992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fan, Wenyuan","contributorId":174007,"corporation":false,"usgs":false,"family":"Fan","given":"Wenyuan","email":"","affiliations":[{"id":6728,"text":"Scripps Inst Oceanography","active":true,"usgs":false}],"preferred":false,"id":800993,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Granja-Bruna, Jose-Luis","contributorId":242871,"corporation":false,"usgs":false,"family":"Granja-Bruna","given":"Jose-Luis","email":"","affiliations":[{"id":48565,"text":"Universidad Complutense de Madrid, Madrid, Spain","active":true,"usgs":false}],"preferred":false,"id":800994,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miller, Nathaniel C. 0000-0003-3271-2929 ncmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-3271-2929","contributorId":174592,"corporation":false,"usgs":true,"family":"Miller","given":"Nathaniel","email":"ncmiller@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800995,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212688,"text":"70212688 - 2020 - Moving beyond p<0.05 in ecotoxicology: A guide for practitioners","interactions":[],"lastModifiedDate":"2020-08-26T12:18:59.072405","indexId":"70212688","displayToPublicDate":"2020-06-15T07:16:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Moving beyond p<0.05 in ecotoxicology: A guide for practitioners","docAbstract":"Statistical inferences play a critical role in ecotoxicology. Historically, Null Hypothesis Significance Testing (NHST) has been the dominant method for inference in ecotoxicology. As a brief and informal definition of the NHST approach, researchers compare (or test) an experimental treatment or observation against a hypothesis of no relationship or effect (the null hypothesis) using the collected data to see if the observed values are statistically significant given predefined error rates. The resulting probability of observing a value equal to or greater than the observed value assuming the null hypothesis is true is the p-value. Historically, criticisms of NHST have existed for almost a century and more recently these have grown to the point where statisticians, including the American Statistical Association, have felt the need to clarify the role of NHST and p-values in science beyond their current, common use. These limitations also exist in ecotoxicology. For example, a review of the 2010 Environmental Toxicology & Chemistry (ET&C) volume found many authors did not correctly report p-values. We repeated this review looking at the 2019 volume of ET&C and the incorrect reporting of p-values still occurred almost a decade later. Problems with NHST and p-values highlight the need for statistical inferences besides NHST, something that has long been known in ecotoxicology and the broader scientific and statistical communities. Furthermore, concerns such as these led the Executive Director of the American Statistical Association to recommend against use of statistical significance in 2019. In light of these criticisms, however, ecotoxicologists require alternative methods. In this paper, we describe some alternative methods including confidence intervals, regression analysis, dose-response curves, Bayes factors, survival analysis, and model selection. Lastly, we provide insights for what ecotoxicology might look like in a post-p-value world.","language":"English","publisher":"Wiley","doi":"10.1002/etc.4800","usgsCitation":"Erickson, R.A., and Rattner, B.A., 2020, Moving beyond p<0.05 in ecotoxicology: A guide for practitioners: Environmental Toxicology and Chemistry, v. 39, no. 9, p. 1657-1669, https://doi.org/10.1002/etc.4800.","productDescription":"13 p.","startPage":"1657","endPage":"1669","ipdsId":"IP-110986","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":377876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":797288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":797289,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217553,"text":"70217553 - 2020 - Investigating the effects of land use and land cover on the relationship between moisture and reflectance using Landsat Time Series","interactions":[],"lastModifiedDate":"2021-01-21T21:00:37.352527","indexId":"70217553","displayToPublicDate":"2020-06-13T14:57:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Investigating the effects of land use and land cover on the relationship between moisture and reflectance using Landsat Time Series","docAbstract":"

To better understand the Earth system, it is important to investigate the interactions between precipitation, land use/land cover (LULC), and the land surface, especially vegetation. An improved understanding of these land-atmosphere interactions can aid understanding of the climate system and modeling of time series satellite data. Here, we investigate the effect of precipitation and LULC on the reflectance of the land surface in the northern U.S. Great Plains. We utilize time series satellite data from the 45 year Landsat archive. The length of the Landsat record allows for analysis of multiple periods of drought and wet conditions (reflecting climate, as well as weather), such that the precipitation-reflectance relationship can be investigated robustly for every individual pixel in the study area. The high spatial resolution of Landsat (30 m) allows for investigation of spatial patterns in weather (i.e., precipitation extremes) interactions with land surface reflectance at the scale of individual fields. Weather history is represented by a drought index that describes effective moisture availability, the Standardized Precipitation and Evaporation Index (SPEI). We find that effective moisture has a robust and consistent effect on reflectance over many types of land cover, with ∼90% of all pixels having significantly ( p&lt;0.01\">p<0.01 ) higher visible reflectance during dry periods than during wet, occurring in nearly all regional, temporal, and LULC categories investigated. In grassland, the relationship is especially strong; there is an average reflectance increase of more than a third between very wet and very dry conditions (red band), and ∼99% of pixels have a significant relationship. In cropland, the effective moisture-reflectance relationship is more variable, suggesting that management decisions are an important factor in cropland-reflectance relationships.

","language":"English","publisher":"MDPI","doi":"10.3390/rs12121919","usgsCitation":"Tollerud, H.J., Brown, J.F., and Loveland, T., 2020, Investigating the effects of land use and land cover on the relationship between moisture and reflectance using Landsat Time Series: Remote Sensing, v. 12, no. 12, 1919, 29 p., https://doi.org/10.3390/rs12121919.","productDescription":"1919, 29 p.","ipdsId":"IP-107717","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":456410,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12121919","text":"Publisher Index Page"},{"id":382440,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota, South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.095703125,\n 43.8503744993026\n ],\n [\n -100.86547851562499,\n 43.8503744993026\n ],\n [\n -100.86547851562499,\n 46.837649560937464\n ],\n [\n -103.095703125,\n 46.837649560937464\n ],\n [\n -103.095703125,\n 43.8503744993026\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Tollerud, Heather J. 0000-0001-9507-4456","orcid":"https://orcid.org/0000-0001-9507-4456","contributorId":210820,"corporation":false,"usgs":true,"family":"Tollerud","given":"Heather","email":"","middleInitial":"J.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":808661,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","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}],"preferred":true,"id":808662,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":808663,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}