Coastal wetlands store carbon dioxide (CO2) and emit CO2 and methane (CH4) making them an important part of greenhouse gas (GHG) inventorying. In the contiguous United States (CONUS), a coastal wetland inventory was recently calculated by combining maps of wetland type and change with soil, biomass, and CH4 flux data from a literature review. We assess uncertainty in this developing carbon monitoring system to quantify confidence in the inventory process itself and to prioritize future research. We provide a value-added analysis by defining types and scales of uncertainty for assumptions, burial and emissions datasets, and wetland maps, simulating 10 000 iterations of a simplified version of the inventory, and performing a sensitivity analysis. Coastal wetlands were likely a source of net-CO2-equivalent (CO2e) emissions from 2006–2011. Although stable estuarine wetlands were likely a CO2e sink, this effect was counteracted by catastrophic soil losses in the Gulf Coast, and CH4 emissions from tidal freshwater wetlands. The direction and magnitude of total CONUS CO2e flux were most sensitive to uncertainty in emissions and burial data, and assumptions about how to calculate the inventory. Critical data uncertainties included CH4 emissions for stable freshwater wetlands and carbon burial rates for all coastal wetlands. Critical assumptions included the average depth of soil affected by erosion events, the method used to convert CH4 fluxes to CO2e, and the fraction of carbon lost to the atmosphere following an erosion event. The inventory was relatively insensitive to mapping uncertainties. Future versions could be improved by collecting additional data, especially the depth affected by loss events, and by better mapping salinity and inundation gradients relevant to key GHG fluxes.
","language":"English","publisher":"IOP","doi":"10.1088/1748-9326/aae157","usgsCitation":"Holmquist, J., Windham-Myers, L., Bernal, B., Byrd, K.B., Crooks, S., Gonneea Eagle, M., Herold, N., Knox, S., Kroeger, K.D., John McCombs, J. Patrick Megonigal, Meng, L., James Morris, Ariana Sutton-Grier, Tiffany Troxler, and Donald Weller, 2018, Uncertainty in United States coastal wetland greenhouse gas inventorying: Environmental Research Letters, v. 13, no. 11, 115005, 16 p., https://doi.org/10.1088/1748-9326/aae157.","productDescription":"115005, 16 p.","ipdsId":"IP-101677","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":468253,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aae157","text":"Publisher Index Page"},{"id":365628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"13","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Holmquist, James","contributorId":217021,"corporation":false,"usgs":false,"family":"Holmquist","given":"James","email":"","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":766255,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":766254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bernal, Blanca","contributorId":217022,"corporation":false,"usgs":false,"family":"Bernal","given":"Blanca","email":"","affiliations":[{"id":39564,"text":"Winrock International","active":true,"usgs":false}],"preferred":false,"id":766256,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":766257,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crooks, Steve","contributorId":217023,"corporation":false,"usgs":false,"family":"Crooks","given":"Steve","email":"","affiliations":[{"id":38182,"text":"Silvestrum Climate Associates","active":true,"usgs":false}],"preferred":false,"id":766258,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gonneea Eagle, Meagan 0000-0001-5072-2755 mgonneea@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":174590,"corporation":false,"usgs":true,"family":"Gonneea Eagle","given":"Meagan","email":"mgonneea@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":766259,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Herold, Nathan","contributorId":217024,"corporation":false,"usgs":false,"family":"Herold","given":"Nathan","email":"","affiliations":[{"id":39565,"text":"NOAA Office for Coastal Management","active":true,"usgs":false}],"preferred":false,"id":766260,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Knox, Sara 0000-0003-2255-5835","orcid":"https://orcid.org/0000-0003-2255-5835","contributorId":216996,"corporation":false,"usgs":false,"family":"Knox","given":"Sara","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":766261,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":766262,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"John McCombs","contributorId":217025,"corporation":false,"usgs":false,"family":"John McCombs","affiliations":[{"id":39565,"text":"NOAA Office for Coastal Management","active":true,"usgs":false}],"preferred":false,"id":766263,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"J. Patrick Megonigal","contributorId":217026,"corporation":false,"usgs":false,"family":"J. Patrick Megonigal","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":766264,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Meng, Lu","contributorId":217027,"corporation":false,"usgs":false,"family":"Meng","given":"Lu","email":"","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":766265,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"James Morris","contributorId":176971,"corporation":false,"usgs":false,"family":"James Morris","affiliations":[],"preferred":false,"id":766266,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ariana Sutton-Grier","contributorId":217028,"corporation":false,"usgs":false,"family":"Ariana Sutton-Grier","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":766267,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tiffany Troxler","contributorId":217029,"corporation":false,"usgs":false,"family":"Tiffany Troxler","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":766268,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Donald Weller","contributorId":217030,"corporation":false,"usgs":false,"family":"Donald Weller","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":766269,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70216312,"text":"70216312 - 2018 - Evaluation of chronic toxicity of sodium chloride or potassium chloride to a unionid mussel (Lampsilis siliquoidea) in water exposures using standard and refined toxicity testing methods","interactions":[],"lastModifiedDate":"2021-05-07T19:15:10.750685","indexId":"70216312","displayToPublicDate":"2018-11-11T12:05:37","publicationYear":"2018","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}},"displayTitle":"Evaluation of chronic toxicity of sodium chloride or potassium chloride to a unionid mussel (Lampsilis siliquoidea) in water exposures using standard and refined toxicity testing methods","title":"Evaluation of chronic toxicity of sodium chloride or potassium chloride to a unionid mussel (Lampsilis siliquoidea) in water exposures using standard and refined toxicity testing methods","docAbstract":"Freshwater mussels are generally underrepresented in toxicity databases used to derive water quality criteria, especially for long‐term exposures. Multiple tests were conducted to determine the chronic toxicity of sodium chloride (NaCl) or potassium chloride (KCl) to a unionid mussel (fatmucket, Lampsilis siliquoidea). Initially, a 4‐wk NaCl test and a 4‐wk KCl test were conducted starting with 2‐mo‐old mussels in water exposures with and without a thin layer of sand substrate. A feeding study was conducted later to refine test conditions for longer‐term 12‐wk exposures, and 3 chronic NaCl tests were then conducted following the refined method to assess the influence of test duration (4–12 wk) and age of organisms (starting age ∼1 wk to 2 mo) on mussel sensitivity. Biomass (total dry wt of surviving mussels in a replicate) was generally a more sensitive endpoint compared to survival and growth (length and dry wt). In the 4‐wk NaCl or KCl test started with 2‐mo‐old juveniles, a 20% effect concentration (EC20) based on biomass (264 mg Cl/L from the NaCl test or 8.7 mg K/L from the KCl test) in the exposure with sand was 2‐fold lower than the EC20 in the exposure without sand. The longer‐term 12‐wk NaCl tests started with the 1‐wk‐old and 2‐mo‐old juveniles were successfully completed under refined test conditions based on the feeding study, and younger juveniles were more sensitive to NaCl than older juveniles. The NaCl toxicity did not substantially change with extended exposure periods from 4 to 12 wk, although the 4‐wk EC20s for biomass were slightly greater (up to 37%) than the 12‐wk EC20s in the 2 longer‐term exposures. Including the toxicity data from the present study into existing databases would rank fatmucket the most sensitive species to KCl and the second most sensitive species to NaCl for all freshwater organisms.
The extensive development of oil and natural-gas resources in south Texas during the past 10 years has led to questions regarding possible environmental effects of processes associated with oil and natural-gas production, in particular the process of hydraulic fracturing, on water and other natural resources. Part of the lower San Antonio River watershed intersects an area of oil and natural-gas production from the sedimentary rocks that compose the Eagle Ford Group.
The rapid expansion of infrastructure associated with oil and natural-gas production increases potential pathways for inorganic and organic contaminants to enter surface-water systems. The U.S. Geological Survey, in cooperation with the San Antonio River Authority, analyzed geospatial data from different years (2008 and 2015) to evaluate changes in land cover associated with oil and natural-gas production activities in the lower San Antonio River watershed. Impervious surface in this study is defined as land cover consisting of well pads, oil- and gas-related features, or roads. The areal coverage associated with impervious surface increased from 201 acres to 5,390 acres (net increase of 5,189 acres) between 2008 and 2015. The total percentage of the study area accounted for by impervious surface resulting from oil and natural-gas production activities increased from 0.034 percent to 0.912 percent, which is an increase of approximately 27-fold. Collectively, 0.878 percent of the study area was converted to new impervious surface between 2008 and 2015. If the area associated with new storage ponds (0.066 percent) is added to the estimate of total land-cover changes as a result of oil and natural-gas production, then 0.944 percent of the study area was altered.
During 2015–17, surface-water samples collected from 5 sites and streambed-sediment samples collected from 17 sites in the lower San Antonio River watershed were analyzed for a broad range of constituents that might be associated with oil and natural-gas production. All major elements, trace elements, semivolatile organic compounds (SVOCs), and volatile organic compounds (VOCs) measured in surface-water samples were detected at concentrations less than any of the U.S. Environmental Protection Agency’s water-quality standards. In general, the greatest SVOC and VOC concentrations were observed in samples collected from sites upstream from the area of active oil and natural-gas production and just downstream from urban areas. The lack of benzene, toluene, ethylbenzene, and all isomers of xylene (hereinafter referred to as BTEX) for most sites within the area of active oil and natural-gas production indicates that little, if any, local runoff associated with the area of active oil and natural-gas production has contaminated the surface water with BTEX compounds. Glycols, which are commonly used in hydraulic fracturing fluids as scale inhibitors, were detected in one surface-water sample from Ecleto Creek within the area of oil and natural-gas production; however, the presence of glycols does not necessarily indicate contamination from hydraulic fracturing fluid. The glycols detected also have other potential sources including the use of diethylene and ethylene glycols in antifreeze used in vehicles and the use of triethylene glycol in antibacterial air sanitizers.
The concentrations of select constituents in the streambed-sediment samples were compared to sediment quality guidelines (SQGs). The SQGs evaluate the potential toxicity of bed sediments to sediment-dwelling organisms. Two SQG concentration levels are used: (1) a lower level, called the threshold effect concentration (TEC), below which harmful effects to benthic biota are not expected, and (2) a higher level, the probable effect concentration (PEC), above which harmful effects are expected to occur frequently. The PEC for arsenic was exceeded in a sample collected from one site on Ecleto Creek. The origin of the elevated arsenic concentration is unknown; the contamination likely is not related to oil and natural-gas production because the site of the sample collection is located upstream from the area of active oil and natural-gas production. Streambed-sediment samples were analyzed for selected polycyclic aromatic hydrocarbons (PAHs) because PAHs can be used as indicators of petroleum hydrocarbons associated with produced waters. Each streambed-sediment sample was analyzed for two size fractions of PAHs: less than (<) 63 micrometers (μm) and < 2 millimeters (mm). Total PAH concentrations in all samples, regardless of size fraction, were less than the TEC for total PAHs of 1,610 micrograms per kilogram. Total PAH concentrations generally were greater in the <63-μm size-fraction samples than in the <2-mm size-fraction samples, indicating that PAHs could potentially sorb more readily to the exclusively silt- and clay-sized particles that compose <63-μm size-fraction samples than to the mixture of silt and clay and larger sized particles that compose the <2-mm size-fraction samples. Total PAH concentrations typically were greater in the samples collected from the sites upstream from the area of active oil and natural-gas production compared to those collected from sites within the area in both the <2-mm and <63-μm size-fraction samples. The smaller PAH concentrations measured in samples collected from within the area of active oil and natural-gas production in comparison to the upstream urbanized areas indicate relatively minor additional local contributions of PAHs of uncertain origin to the watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185119","collaboration":"Prepared in cooperation with the San Antonio River Authority","usgsCitation":"Crow, C.L., Opsahl, S.P., Pedraza, D.E., Pease, E.C., and Kushnereit, R.K., 2018, Land-cover changes associated with oil and natural-gas production and concentrations of selected constituents in surface-water and streambed-sediment samples collected upstream from and within an area of oil and natural-gas production, south Texas, 2008–17: U.S. Geological Survey Scientific Investigations Report 2018–5119, 52 p., https://doi.org/10.3133/sir20185119.","productDescription":"Report: vii, 52 p.; Data Release","numberOfPages":"64","onlineOnly":"N","ipdsId":"IP-095610","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":437689,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74J0DDQ","text":"USGS data release","linkHelpText":"Land-Cover, Surface-water, and Streambed-sediment data Collected Upstream from and Within an Area of Oil and Natural-Gas Production, South Texas, 2008-17"},{"id":359271,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F74J0DDQ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Land-use, water-quality, and sediment-quality data from an area upstream from and within an area of oil and natural-gas production in the lower San Antonio River watershed, south Texas, 2008–17"},{"id":359269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5119/coverthb.jpg"},{"id":359270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5119/sir20185119.pdf","text":"Report","size":"4.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5119"}],"country":"United States","state":"Texas","otherGeospatial":"San Antonio River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.38531494140624,\n 28.459033019728043\n ],\n [\n -96.91314697265625,\n 28.459033019728043\n ],\n [\n -96.91314697265625,\n 29.652255607121884\n ],\n [\n -98.38531494140624,\n 29.652255607121884\n ],\n [\n -98.38531494140624,\n 28.459033019728043\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Volcanic plumes are challenging to detect and characterize rapidly, but insights into processes such as hail formation or ash aggregation are valuable to hazard forecasts during volcanic crises. Global Navigation Satellite System (GNSS, which includes GPS) signals traveling from satellites to ground receivers can be disturbed by volcanic plumes. To date, two effects aiding plume detection from GNSS observations have been described: (a) ash‐rich plumes scatter the signal, lowering the signal‐to‐noise ratio (SNR), and (b) some plumes refract and thus delay GNSS signals. Using GNSS data from the VEI 4 2011 Grímsvötn eruption, we show that tephra and water contents of plumes distinctly affect SNR and phase residuals. The signals suggest high‐altitude freezing of plume water into volcanic hail—corroborated by 1‐D modeling and volcanic hail deposits. Combining GNSS SNR and phase residual analyses is valuable for detecting processes that rapidly scrub fine ash out of the atmosphere.
Sheet 1: This image mosaic is based on observations acquired by the Mercury Dual Imaging System (MDIS; Hawkins and others, 2009), an instrument on the National Aeronautics and Space Agency (NASA) MErcury Surface, Space ENvironment, Geochemistry, and Ranging (MESSENGER) spacecraft (Solomon and others, 2007). The Mercator projection is used between latitudes ±57°, with a central meridian at 0° longitude and latitude equal to the nominal scale at 0°. The polar stereographic projection is used for the regions north of the +55° parallel and south of the –55° parallel, with a central meridian set for both at 0° and a latitude of true scale at +90° and –90°, respectively. All features greater than 100 km in diameter or length were included unless they were not visible at the printed map scale. Some selected well-known features less than 100 km in diameter or length were also included. For listed references, please open the full PDF.
Sheet 2: This map is based on data acquired by the Mercury Dual Imaging System (MDIS; Hawkins and others, 2009) and Mercury Laser Altimeter (MLA; Cavanaugh and others, 2007) instruments on the National Aeronautics and Space Agency (NASA) MErcury Surface, Space ENvironment, Geochemistry, and Ranging (MESSENGER) spacecraft (Solomon and others, 2007). The topographic shaded-relief maps were generated from the original MDIS- and MLA-based DEMs with a sun angle of 45° from horizontal and a sun azimuth of 270°, as measured clockwise from north, with no vertical exaggeration. The DEM values were then mapped to a global color look-up table, with each color representing a range of 1 km of elevation. The shaded-relief and color files were then merged and scaled to 1:20,000,000 for the Mercator portion and 1:12,157,366 for the two polar stereographic parts with a resolution of 300 pixels per inch. The two projections have a common scale at ±56° latitude. The Mercator projection is used between latitudes ±57°, with a central meridian at 0° longitude and latitude equal to the nominal scale at 0°. The polar stereographic projection is used for the regions north of the +55° parallel and south of the –55° parallel, with a central meridian set for both at 0° and a latitude of true scale at +90° and –90°, respectively. All features greater than 200 km in diameter or length were included unless they were not visible at the printed map scale. Some selected well-known features less than 200 km in diameter or length were also included. Sheet 2 is offered digitally as a layered PDF with two elevation color ramp options—the original printed version and a multicolored ramp developed by the MESSENGER team for their global products. For listed references, please open the full PDF.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3404","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Hunter, M.A., Hare, T.M., Hayward, R.K., Chabot, N.L., Hash, C.D., Denevi, B.W., Ernst, C.M., Murchie, S.L., Blewett, D.T., Malaret, E.R., Solomon, S.C., Becker, K.J., Becker, T.L., Weller, L.A., Edmundson, K.L., Neuman, G.A., Mazarico, E., and Perry, M.E., 2018, Image mosaic and topographic maps of Mercury: U.S. Geological Survey Scientific Investigations Map 3404, scale 1:20,000,000 and 1:12,157,366, https://doi.org/10.3133/sim3404.","productDescription":"2 Sheets: 34.95 x 33.38 inches and 35.0 x 38.0 inches","ipdsId":"IP-089995","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":359290,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3404/sim3404_sheet1_.pdf","text":"Sheet 1","size":"8.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3404 Sheet 1","linkHelpText":" - Image Map of Mercury"},{"id":359291,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3404/sim3404_sheet2_.pdf","text":"Sheet 2","size":"15.7 MB layered","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3404 Sheet 2","linkHelpText":" - Topographic Map of Mercury"},{"id":359289,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3404/coverthb2.jpg"}],"otherGeospatial":"Mercury","contact":"Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
In many simulations of historical daily streamflow distributional bias arising from the distributional properties of residuals has been noted. This bias often presents itself as an underestimation of high streamflow and an overestimation of low streamflow. Here, 1168 streamgages across the conterminous USA, having at least 14 complete water years of daily data between 1 October 1980 and 30 September 2013, are used to explore a method for rescaling simulated streamflow to correct the distributional bias. Based on an existing approach that separates the simulated streamflow into components of temporal structure and magnitude, the temporal structure is converted to simulated nonexceedance probabilities and the magnitudes are rescaled using an independently estimated flow duration curve (FDC) derived from regional regression. In this study, this method is applied to a pooled ordinary kriging simulation of daily streamflow coupled with FDCs estimated by regional regression on basin characteristics. The improvement in the representation of high and low streamflows is correlated with the accuracy and unbiasedness of the estimated FDC. The method is verified by using an idealized case; however, with the introduction of regionally regressed FDCs developed for this study, the method is only useful overall for the upper tails, which are more accurately and unbiasedly estimated than the lower tails. It remains for future work to determine how accurate the estimated FDCs need to be to be useful for bias correction without unduly reducing accuracy. In addition to its potential efficacy for distributional bias correction, this particular instance of the methodology also represents a generalization of nonlinear spatial interpolation of daily streamflow using FDCs. Rather than relying on single index stations, as is commonly done to reflect streamflow timing, this approach to simulation leverages geostatistical tools to allow a region of neighbors to reflect streamflow timing.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-22-5741-2018","usgsCitation":"Farmer, W.H., Over, T.M., and Kiang, J.E., 2018, Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves: Hydrology and Earth System Sciences, v. 22, p. 5741-5758, https://doi.org/10.5194/hess-22-5741-2018.","productDescription":"18 p.","startPage":"5741","endPage":"5758","ipdsId":"IP-092889","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":468256,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-22-5741-2018","text":"Publisher Index Page"},{"id":360785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"22","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Farmer, William H. 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":4374,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","middleInitial":"H.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":755284,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Over, Thomas M. 0000-0001-8280-4368 tmover@usgs.gov","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":1819,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"tmover@usgs.gov","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755285,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiang, Julie E. 0000-0003-0653-4225 jkiang@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-4225","contributorId":2179,"corporation":false,"usgs":true,"family":"Kiang","given":"Julie","email":"jkiang@usgs.gov","middleInitial":"E.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":755286,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70200225,"text":"sir20185133 - 2018 - Hydrology-driven chemical loads transported by the Green River to the Lower Duwamish Waterway near Seattle, Washington, 2013–17","interactions":[],"lastModifiedDate":"2018-11-14T15:57:29","indexId":"sir20185133","displayToPublicDate":"2018-11-08T11:19:18","publicationYear":"2018","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":"2018-5133","displayTitle":"Hydrology-Driven Chemical Loads Transported by the Green River to the Lower Duwamish Waterway near Seattle, Washington, 2013–17","title":"Hydrology-driven chemical loads transported by the Green River to the Lower Duwamish Waterway near Seattle, Washington, 2013–17","docAbstract":"The sediments in the Lower Duwamish Waterway Superfund site in Seattle, Washington, are contaminated with chemicals including metals such as arsenic, polychlorinated biphenyls (PCBs), carcinogenic polycyclic aromatic hydrocarbons (cPAHs), and dioxins/furans from decades of intense anthropogenic activities. The U.S. Geological Survey, in cooperation with the Washington State Department of Ecology, collected new data from 2013 to 2017 to estimate sediment and chemical loads transported by the Green/Duwamish River to the Lower Duwamish Waterway Superfund site (the final 8-kilometer reach of the river) in support of sediment remediation within the site. Chemical loads were calculated as the product of river suspended-sediment loads and suspended sediment-bound chemical concentrations measured at river kilometer 16.7.
Using four different approaches, annual suspended sediment-bound chemical load estimates transported by the river to the Lower Duwamish Waterway were in the range of 1,120–1,470 kilograms arsenic, 2,810–8,200 grams (g) toxic equivalent cPAHs, 205–407 milligrams toxic equivalent dioxins/furans, and 340–1,180 g PCBs. Storm events contributed a disproportionately large amount of the load of anthropogenic organic compounds such as cPAHs (54 percent), dioxins/furans (44 percent), and PCBs (52 percent) as compared to overall time (17 percent).
Chemical concentrations and load estimates often were underestimated using results from unfiltered water analysis only, especially in samples with high suspended-sediment concentrations and for hydrophobic organic chemicals such as cPAHs that prefer to sorb to particulates and are at low concentrations near or below the analytical limits of water methods. For metals and PCBs, the dissolved concentration was relatively low and consistent between sampling events, whereas the suspended sediment-bound chemical concentrations contributed most of the chemical concentration in the water column during periods of high river suspended-sediment concentrations. However, the dissolved fraction, on average, contributed more than one-third of the estimated total chemical load in the river system for arsenic and PCBs, even given the hydrophobic nature of the chemicals. These results suggest that the sum of the chemical concentrations measured on two separate fractions—the particulate fraction and the dissolved fraction—more fully represents the total chemical concentration as compared to analysis of an unfiltered water sample, especially in samples with high suspended-sediment concentrations.
Most of the suspended-sediment load (97 percent) and sediment-bound chemical load (92–94 percent) occurred during the wet winter half of the year from October 15 to April 14. However, the highest sediment-bound chemical concentrations often occurred during short intense storms or “first flush” autumn runoff events during the dry summer half of the year from April 15 to October 14. Because of the highly variable and dynamic river system characteristics (including precipitation, discharge, sediment concentration, and tidal fluctuations), it is critical to characterize the occurrence, frequency, concentrations, and loads during extreme conditions (for example, when the river is affected by storm-derived runoff) rather than time-averaged conditions. These short extreme events have a high potential for acute effects on ecological and human health, and may have a great influence on the effectiveness of the sediment remediation activities that are underway in the Lower Duwamish Waterway.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185133","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Conn, K.E., Black, R.W., Senter, C.A., Peterson, N.T., and Vanderpool-Kimura, A., 2018, Hydrology-driven chemical loads transported by the Green River to the Lower Duwamish Waterway near Seattle, Washington, 2013–17: U.S. Geological Survey Scientific Investigations Report 2018-5133, 37 p., https://doi.org/10.3133/sir20185133.","productDescription":"vii, 37 p.","onlineOnly":"Y","ipdsId":"IP-099196","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":359329,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5133/coverthb2.jpg"},{"id":359330,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5133/sir20185133.pdf","text":"Report","size":"6.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5133"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Green-Duwamish River, Lower Duwanish Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.39627838134766,\n 47.458272792347074\n ],\n [\n -122.22290039062499,\n 47.458272792347074\n ],\n [\n -122.22290039062499,\n 47.59875528481801\n ],\n [\n -122.39627838134766,\n 47.59875528481801\n ],\n [\n -122.39627838134766,\n 47.458272792347074\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
GenEst (a generalized estimator of mortality) is a suite of statistical models and software tools for generalized mortality estimation. It was specifically designed for estimating the number of bird and bat fatalities at solar and wind power facilities, but both the software (Dalthorp and others, 2018) and the underlying statistical models are general enough to be useful in various situations to estimate the size of open populations when detection probabilities and search coverages are less than 1. In this report, we outline the statistical models and data structures underlying the estimator. The models are numerous, complex, and intricately interwoven. Discussion begins with broad, high-level overviews of the general models. The lower-level technical details are then gradually added. Broader and less technical discussions on the general context and applications of the models and the use of the software are available in the software user guide (Simonis and others, 2018), vignettes bundled with the software, and the help files within the software itself.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Algorithm in Book 7:Automated data processing and computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7A2","collaboration":"Prepared in cooperation with Bureau of Land Management and the National Renewable Energy Laboratory","usgsCitation":"Dalthorp, D., Madsen, L., Huso, M., Rabie, P., Wolpert, R., Studyvin, J., Simonis, J., and Mintz, J., 2018, GenEst statistical models—A generalized estimator of mortality: U.S. Geological Survey Techniques and Methods, book 7, chap. A2, 13 p., https://doi.org/10.3133/tm7A2.","productDescription":"Report: iv, 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-101458","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":359137,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/7a2/coverthb.jpg"},{"id":359138,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/7a2/tm7a2.pdf","text":"Report","size":"371 KB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-A2"},{"id":359326,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9BATL","text":"USGS data release","linkHelpText":"Generalized Mortality Estimator (GenEst) - R code & GUI"},{"id":359139,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm7C19","text":"Techniques and Methods 7-C19 —","description":"TM 7-C19","linkHelpText":"GenEst User Guide—Software for a Generalized Estimator of Mortality"}],"publicComments":"This report is Chapter 2 of Section A: Algorithm in Book 7:Automated data processing and computations.","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Wind and other energy development are expanding rapidly and on an unprecedented scale within the range of the Golden Eagle (Aquila chrysaetos) while other anthropogenic-related changes, wildfires, invasive plants, drought, and climate change are altering or destroying native habitats occupied by Golden Eagles. However, the potential effects of these factors on North American Golden Eagle populations are largely unknown and the most recent evidence indicates that the population in the western United States is declining slightly. Impediments to evaluating the potential effects of energy development projects on wintering Golden Eagles include issues of scale and a paucity of available information about eagle winter use areas and ecology. We applied a predictive model of eagle winter distribution developed for Idaho and Montana, to Idaho, Utah, Nevada and eastern Oregon to help identify potential wintering areas and identify risks that occur in those areas. The model identifies ~40% of the four state study area as potentially suitable eagle winter habitat and provides a basis for spatial assessment of possible risk factors to eagles wintering there. We used eBird and Christmas Bird Count citizen science datasets for an independent evaluation of the accuracy of our predictive distribution model. The model was robust, accurately predicting the presence of wintering Golden Eagles significantly more often than expected. We used digital environmental datasets (layers) of potential risk factors, in conjunction with model predicted eagle distribution, to better understand and estimate the extent of risks to the wintering eagle population in the study area. These layers represent available data for some of the factors previously identified as risks in the landscape to wintering Golden Eagles. The majority of predicted eagle wintering areas occurred where there was little habitat fragmentation (<10%). All predicted winter areas contained at least one potential risk factor (e.g., potential for energy development); 39.4% of predicted winter areas contained at least two known risk factors. The greatest number of risks often occurred where the human footprint was highest and where eagles were less likely to occur during winter. Our results can be used to help prioritize field surveys for identifying important Golden Eagle winter areas in the western United States and determine potential locations where energy development is least likely to have negative effects on wintering eagles. Survey efforts can be allocated in consideration of management and conservation objectives based on predicted habitat suitability and risk factors. For example, surveys for areas of high suitability and low risk can identify places to focus management for conservation of eagle winter areas. Further, sites proposed for wind energy development could be reviewed initially based on model predicted eagle wintering areas and then surveyed to determine if permitting for development is appropriate.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Machine learning for ecology and sustainable natural resource management","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-96978-7_19","usgsCitation":"Craig, E.H., Fuller, M.R., Craig, T.H., and Huettmann, F., 2018, Assessment of potential risks from renewable energy development and other anthropogenic factors to wintering Golden Eagles in the western United States, chap. of Machine learning for ecology and sustainable natural resource management, p. 379-407, https://doi.org/10.1007/978-3-319-96978-7_19.","productDescription":"29 p.","startPage":"379","endPage":"407","ipdsId":"IP-097959","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":361650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-06","publicationStatus":"PW","contributors":{"editors":[{"text":"Humphries, Grant","contributorId":213887,"corporation":false,"usgs":false,"family":"Humphries","given":"Grant","email":"","affiliations":[],"preferred":false,"id":758612,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Magness, Dawn","contributorId":147692,"corporation":false,"usgs":false,"family":"Magness","given":"Dawn","affiliations":[{"id":16903,"text":"U.S. Fish and Wildlife Service, Kenai National Wildlife Refuge, Soldotna, AK, 99669, USA","active":true,"usgs":false}],"preferred":false,"id":758613,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Huettmann, Falk","contributorId":15663,"corporation":false,"usgs":false,"family":"Huettmann","given":"Falk","email":"","affiliations":[],"preferred":false,"id":758614,"contributorType":{"id":2,"text":"Editors"},"rank":3}],"authors":[{"text":"Craig, Erica H.","contributorId":176469,"corporation":false,"usgs":false,"family":"Craig","given":"Erica","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":758021,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Mark R. 0000-0001-7459-1729 mark_fuller@usgs.gov","orcid":"https://orcid.org/0000-0001-7459-1729","contributorId":2296,"corporation":false,"usgs":true,"family":"Fuller","given":"Mark","email":"mark_fuller@usgs.gov","middleInitial":"R.","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}],"preferred":true,"id":758022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Craig, Tim H.","contributorId":213558,"corporation":false,"usgs":false,"family":"Craig","given":"Tim","email":"","middleInitial":"H.","affiliations":[{"id":27672,"text":"Aquila Environmental","active":true,"usgs":false}],"preferred":false,"id":758023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huettmann, Falk","contributorId":15663,"corporation":false,"usgs":false,"family":"Huettmann","given":"Falk","email":"","affiliations":[],"preferred":false,"id":758024,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199978,"text":"sir20185137 - 2018 - Revised groundwater-flow model of the glacial aquifer system north of Aberdeen, South Dakota, through water year 2015","interactions":[],"lastModifiedDate":"2019-03-27T11:06:00","indexId":"sir20185137","displayToPublicDate":"2018-11-06T08:06:51","publicationYear":"2018","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":"2018-5137","displayTitle":"Revised Groundwater-flow Model of the Glacial Aquifer System North of Aberdeen, South Dakota, Through Water Year 2015","title":"Revised groundwater-flow model of the glacial aquifer system north of Aberdeen, South Dakota, through water year 2015","docAbstract":"The city of Aberdeen, in northeastern South Dakota, requires an expanded and sustainable supply of water to meet current and future demands. Conceptual and numerical models of the glacial aquifer system in the area north of Aberdeen were developed by the U.S. Geological Survey in cooperation with the City of Aberdeen in 2012. The U.S. Geological Survey, in cooperation with the City of Aberdeen, completed a study to revise the original numerical groundwater-flow model using data through water year (WY) 2015 to aid the City of Aberdeen in their development of plans and strategies for a sustainable water supply and to increase understanding of the glacial aquifer system and groundwater-flow system near Aberdeen. The original model was revised to improve the fit between model-simulated values and observed (measured or estimated) data, provide greater insight into surface-water interactions, and improve the usefulness of the model for water-supply planning. The revised groundwater-flow model (hereafter referred to as the “revised model”) presented in this report supersedes the original model.
The purpose of this report is to describe a revised groundwater-flow model including data collection, model calibration, and model results for the glacial aquifer system including the Elm, Middle James, and Deep James aquifers north of Aberdeen, South Dakota, using updated hydrologic data through WY 2015. The original numerical model was revised in several ways. The model was modified by adding four new layers, which included a surficial layer, two intervening confining layers, and a shale bedrock layer. The revised model provides an improved understanding of the groundwater-flow system in comparison to the original model.
The principal aquifers of the model area include portions of the Elm, Middle James, and Deep James aquifers. The lithologic information used to define and describe the aquifers in the model area was unaltered; however, aquifer properties and boundary conditions were reviewed and updated using geological information reported by the South Dakota Department of Environmental and Natural Resources and information obtained from geophysical investigations for this study. The horizontal extent of the Elm, Middle James, and Deep James aquifers was unaltered from the original model. The thickness of the Deep James aquifer was modified based on interpretations from the geophysical investigations. In general, groundwater in the Elm aquifer flowed from northwest to southeast and locally towards rivers and streams. Similarly, in the Middle James and Deep James aquifers, groundwater also typically flowed southeast.
The revisions made to the original model include use of the following MODFLOW stress packages: Recharge, Evapotranspiration, Time-Variant Specified Head, Wells, Drains, and Stream Flow Routing, all of which were updated from the original model except for the Stream Flow Routing Package, which replaced the River Package used in the original model. Model calibration is the process of estimating model parameters to minimize the differences, or residuals, between observed data and simulated values; therefore, Parameter ESTimation (PEST) software was used to optimize model input parameters by matching model-simulated values to observed data. Calibration parameters included horizontal hydraulic conductivity, vertical hydraulic conductivity, specific yield, specific storage, and vertical streambed conductance for stream and drain cells. Multipliers were used to calibrate the recharge and evapotranspiration stresses. Evapotranspiration extinction depth also was adjusted during model calibration.
Comparisons to the original model are described to highlight the changes made in the revised model. In general, the revised model adequately simulates the natural system and compares favorably with observed hydrologic data. Simulated water levels were evaluated by comparing them to single water-level observations at selected well locations. The selected wells were the same wells used in the original model. The coefficient of determination value between simulated and observed water levels for the revised model was 0.89 and included simulated and observed values from October 1, 1974 (WY 1975), through September 30, 2015 (WY 2015). The coefficient of determination value for the original model was 0.94 and included simulated and observed values from October 1, 1974, through September 30, 2009. The difference may indicate that the original model could have been overfit to hydraulic head observations because base flow was not simulated. The additional data used in the revised model included some climatically wetter, more extreme periods, such as 2011, in which annual precipitation was 30.9 inches. Average annual precipitation for the original model timeframe, which included data from WYs 1975–2009, was 20.26 inches. Additional precipitation data for WYs 2010–15, included in the revised model timeframe, resulted in an average annual precipitation for WYs 1975–2015 in the model area of 20.6 inches. The larger variability in climate data coupled with the additional water-level data could explain the lower coefficient of determination for water levels in the revised model.
The revised model was used to calculate various groundwater-budget components for steady-state and transient conditions for WYs 1975–2015. The time-variant specified-head cells in the revised model had the largest change when compared to the original steady-state model for inflows and outflows. Comparing the transient budget components between the original and the revised models indicated that inflow from recharge and time-variant specified-head cells had the greatest effect on groundwater inflows, and outflow from storage had the greatest effect on groundwater outflows. The simulated potentiometric contours from the revised model were compared with (1) the observed (interpreted) potentiometric surface (layer 2) and the hydraulic head values (layers 4 and 6) and (2) the simulated contours from the original model. The simulated hydraulic gradients and general direction of groundwater flow in the Elm aquifer in the revised model generally matched the observed potentiometric contours, the simulated potentiometric contours from the original model, and general flow directions interpreted to be perpendicular to the contours. Minor discrepancies between simulated potentiometric contours from the revised model and the observed potentiometric contours may be due to the lack of observed data in the model area.
The revised model was designed to reduce the limitations of the original model. The revisions were validated by comparing the results of the original model with the revised model. A primary benefit of the revised model is the inclusion of the surficial deposits and the confining units as explicit layers in the model. The addition of the surficial layer was beneficial for three primary reasons: (1) more accurate representation of recharge from precipitation, (2) more accurate representation of groundwater evapotranspiration, and (3) more accurate representation of groundwater and surface-water interactions. The groundwater model is a numeric approximation of a complex physical hydrologic system, and the revised model data were interpolated in regions with sparse data. Additionally, model discretization included averaged and interpolated values for water use, withdrawal rates, and hydraulic conductivity. The revised model provides a useful estimate for hydraulic gradients, groundwater-flow directions, and aquifer response to groundwater withdrawals.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185137","collaboration":"Prepared in cooperation with the City of Aberdeen","usgsCitation":"Valder, J.F., Eldridge, W.G., Davis, K.W., Medler, C.J., and Koth, K.R., 2018, Revised groundwater-flow model of the glacial aquifer system north of Aberdeen, South Dakota, through water year 2015: U.S. Geological Survey Scientific Investigations Report 2018–5137, 56 p., https://doi.org/10.3133/sir20185137.","productDescription":"Report: viii, 56 p.; Data Release","numberOfPages":"68","onlineOnly":"Y","ipdsId":"IP-080010","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":359157,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JVNFLY","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-NWT model of the glacial aquifer system north of Aberdeen, South Dakota, through water year 2015"},{"id":359156,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5137/sir20185137.pdf","text":"Report","size":"4.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5137"},{"id":359155,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5137/coverthb.jpg"}],"country":"United States","state":"South Dakota","city":"Aberdeen","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.6,\n 45.45\n ],\n [\n -98.27,\n 45.45\n ],\n [\n -98.27,\n 45.7\n ],\n [\n -98.6,\n 45.7\n ],\n [\n -98.6,\n 45.45\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
1608 Mountain View Road
Rapid City, SD 57702
The U.S. Geological Survey (USGS) has developed the National Land Cover Database (NLCD) to provide consistent land cover and land cover change products for the nation since 2001. As one of products in the NLCD, the percent impervious surface area (ISA), which was estimated with Landsat imagery, represents the fraction of human-made impervious area in a 30-m grid and has been used to quantify urban land cover types and extents for the United States. However, it is still a challenge to clearly determine urban land cover intensity and extents using remote sensing data with spatial and spectral resolutions similar to Landsat in part because of highly heterogeneous features of urban land cover. Most urban areas, especially in low intensity development areas, exhibit sub-pixel characteristics that mix impervious surface with other land covers (e.g., grass and trees) in the 30-m resolution satellite imagery. Furthermore, the influence of highly heterogeneous features in many urban areas and how they alter the spectral signature of urban landscapes has not yet been fully studied. Recent advances in remote sensing technology have provided multiple spectral and spatial resolution data from several satellites including WorldView (WV), Sentinel-2, and the Landsat Operational Land Imager (OLI). Remote sensing images having different spectral bands and high spatial resolution provide the potential to derive detailed information on the nature and properties of different surface materials on the urban ground. This study focuses on performance of mapping impervious surface using data collected from WorldView-3, Sentinel-2, and Landsat OLI. We compared ISA results estimated from these sensors and evaluated benefits and limitations of radiometric and spatial resolutions for mapping impervious surface in a study area on the Eastern corridor between Washington, D.C., and Baltimore, where developed impervious surface containing both residential housings, office buildings, and roads, in the United States. The impact of different band combinations in Sentinel-2 imagery on mapping urban impervious surface and urban land cover was also evaluated.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","conferenceDate":"Jul 22-27, 2018","conferenceLocation":"Valencia, Spain","language":"English","publisher":"IEEE","doi":"10.1109/IGARSS.2018.8518013","usgsCitation":"Xian, G.Z., Shi, H., Dewitz, J., and Wu, Z., 2018, Analysis of different sensor performances in impervious surface mapping, in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, Jul 22-27, 2018, p. 8189-8192, https://doi.org/10.1109/IGARSS.2018.8518013.","productDescription":"4 p.","startPage":"8189","endPage":"8192","ipdsId":"IP-093474","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":377825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":796923,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dewitz, Jon 0000-0002-0458-212X dewitz@usgs.gov","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":2401,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","email":"dewitz@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":797261,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":796924,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70200814,"text":"70200814 - 2018 - Multi-state occupancy models of foraging habitat use by the Hawaiian hoary bat Lasiurus cinereus semotus","interactions":[],"lastModifiedDate":"2018-11-13T13:24:06","indexId":"70200814","displayToPublicDate":"2018-11-05T09:12:19","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Multi-state occupancy models of foraging habitat use by the Hawaiian hoary bat Lasiurus cinereus semotus","title":"Multi-state occupancy models of foraging habitat use by the Hawaiian hoary bat Lasiurus cinereus semotus","docAbstract":"Multi-state occupancy modeling can often improve assessments of habitat use and site quality when animal activity or behavior data are available. We examine the use of the approach for evaluating foraging habitat suitability of the endangered Hawaiian hoary bat (Lasiurus cinereus semotus) from classifications of site occupancy based on flight activity levels and feeding behavior. In addition, we used data from separate visual and auditory sources, namely thermal videography and acoustic (echolocation) detectors, jointly deployed at sample sites to compare the effectiveness of each method in the context of occupancy modeling. Video-derived observations demonstrated higher and more accurate estimates of the prevalence of high bat flight activity and feeding events than acoustic sampling methods. Elevated levels of acoustic activity by Hawaiian hoary bats were found to be related primarily to beetle biomass in this study. The approach may have a variety of applications in bat research, including inference about species-resource relationships, habitat quality and the extent to which species intensively use areas for activities such as foraging.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0205150","usgsCitation":"Gorresen, P., Brinck, K.W., DeLisle, M.A., Montoya-Aiona, K., Pinzari, C., and Bonaccorso, F., 2018, Multi-state occupancy models of foraging habitat use by the Hawaiian hoary bat Lasiurus cinereus semotus: PLoS ONE, v. 13, no. 10, p. 1-14, https://doi.org/10.1371/journal.pone.0205150.","productDescription":"e0205150; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-099393","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":460815,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0205150","text":"Publisher Index Page"},{"id":437695,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PPSHLW","text":"USGS data release","linkHelpText":"Oahu multi-state occupancy models of foraging habitat use by Hawaiian hoary bats 2017"},{"id":359218,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"10","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-31","publicationStatus":"PW","scienceBaseUri":"5be2b6b0e4b0b3fc5cf5b0bf","contributors":{"authors":[{"text":"Gorresen, P. Marcos 0000-0002-0707-9212","orcid":"https://orcid.org/0000-0002-0707-9212","contributorId":196628,"corporation":false,"usgs":false,"family":"Gorresen","given":"P. Marcos","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":750750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":150936,"corporation":false,"usgs":false,"family":"Brinck","given":"Kevin","email":"kbrinck@usgs.gov","middleInitial":"W.","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":750751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeLisle, Megan A.","contributorId":210453,"corporation":false,"usgs":false,"family":"DeLisle","given":"Megan","email":"","middleInitial":"A.","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":750752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Montoya-Aiona, Kristina 0000-0002-1776-5443 kmontoya-aiona@usgs.gov","orcid":"https://orcid.org/0000-0002-1776-5443","contributorId":5899,"corporation":false,"usgs":true,"family":"Montoya-Aiona","given":"Kristina","email":"kmontoya-aiona@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":750753,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pinzari, Corinna A. 0000-0001-9794-7564","orcid":"https://orcid.org/0000-0001-9794-7564","contributorId":208455,"corporation":false,"usgs":false,"family":"Pinzari","given":"Corinna A.","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":750754,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bonaccorso, Frank 0000-0002-5490-3083 fbonaccorso@usgs.gov","orcid":"https://orcid.org/0000-0002-5490-3083","contributorId":143709,"corporation":false,"usgs":true,"family":"Bonaccorso","given":"Frank","email":"fbonaccorso@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":750749,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70200824,"text":"70200824 - 2018 - Using research networks to create the comprehensive datasets needed to assess nutrient availability as a key determinant of terrestrial carbon cycling","interactions":[],"lastModifiedDate":"2019-01-28T08:51:33","indexId":"70200824","displayToPublicDate":"2018-11-05T08:56:28","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Using research networks to create the comprehensive datasets needed to assess nutrient availability as a key determinant of terrestrial carbon cycling","docAbstract":"A wide range of research shows that nutrient availability strongly influences terrestrial carbon (C) cycling and shapes ecosystem responses to environmental changes and hence terrestrial feedbacks to climate. Nonetheless, our understanding of nutrient controls remains far from complete and poorly quantified, at least partly due to a lack of informative, comparable, and accessible datasets at regional-to-global scales. A growing research infrastructure of multi-site networks are providing valuable data on C fluxes and stocks and are monitoring their responses to global environmental change and measuring responses to experimental treatments. These networks thus provide an opportunity for improving our understanding of C-nutrient cycle interactions and our ability to model them. However, coherent information on how nutrient cycling interacts with observed C cycle patterns is still generally lacking. Here, we argue that complementing available C-cycle measurements from monitoring and experimental sites with data characterizing nutrient availability will greatly enhance their power and will improve our capacity to forecast future trajectories of terrestrial C cycling and climate. Therefore, we propose a set of complementary measurements that are relatively easy to conduct routinely at any site or experiment and that, in combination with C cycle observations, can provide a robust characterization of the effects of nutrient availability across sites. In addition, we discuss the power of different observable variables for informing the formulation of models and constraining their predictions. Most widely available measurements of nutrient availability often do not align well with current modelling needs. This highlights the importance to foster the interaction between the empirical and modelling communities for setting future research priorities.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/aaeae7","usgsCitation":"Vicca, S., Stocker, B., Reed, S.C., Wieder, W.R., Bahn, M., Fay, P.A., Janssens, I., Lambers, H., Penuelas, J., Piao, S., Rebel, K., Sardans, J., Sigurdsson, B.D., Van Sundert, K., Wang, Y., Zaehle, S., and Ciais, P., 2018, Using research networks to create the comprehensive datasets needed to assess nutrient availability as a key determinant of terrestrial carbon cycling: Environmental Research Letters, v. 13, p. 1-13, https://doi.org/10.1088/1748-9326/aaeae7.","productDescription":"Article 125006; 13 p.","startPage":"1","endPage":"13","ipdsId":"IP-101808","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468261,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aaeae7","text":"Publisher Index Page"},{"id":359217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-07","publicationStatus":"PW","scienceBaseUri":"5be2b6b0e4b0b3fc5cf5b0c1","contributors":{"authors":[{"text":"Vicca, Sara","contributorId":169514,"corporation":false,"usgs":false,"family":"Vicca","given":"Sara","email":"","affiliations":[{"id":25541,"text":"University of Antwerp, Belgium","active":true,"usgs":false}],"preferred":false,"id":750781,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stocker, Benjamin","contributorId":169502,"corporation":false,"usgs":false,"family":"Stocker","given":"Benjamin","email":"","affiliations":[{"id":25536,"text":"Imperial College, UK","active":true,"usgs":false}],"preferred":false,"id":750804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":750780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wieder, William R.","contributorId":75792,"corporation":false,"usgs":true,"family":"Wieder","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":750805,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bahn, Michael","contributorId":210470,"corporation":false,"usgs":false,"family":"Bahn","given":"Michael","email":"","affiliations":[],"preferred":false,"id":750806,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fay, Philip A.","contributorId":51443,"corporation":false,"usgs":true,"family":"Fay","given":"Philip","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":750807,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Janssens, Ivan","contributorId":169508,"corporation":false,"usgs":false,"family":"Janssens","given":"Ivan","affiliations":[{"id":25541,"text":"University of Antwerp, Belgium","active":true,"usgs":false}],"preferred":false,"id":750808,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lambers, Hans","contributorId":80165,"corporation":false,"usgs":true,"family":"Lambers","given":"Hans","email":"","affiliations":[],"preferred":false,"id":750809,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Penuelas, Josep","contributorId":177422,"corporation":false,"usgs":false,"family":"Penuelas","given":"Josep","affiliations":[],"preferred":false,"id":750810,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Piao, Shilong","contributorId":105424,"corporation":false,"usgs":true,"family":"Piao","given":"Shilong","email":"","affiliations":[],"preferred":false,"id":750811,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rebel, Karin","contributorId":169512,"corporation":false,"usgs":false,"family":"Rebel","given":"Karin","email":"","affiliations":[{"id":25545,"text":"Utrecht University, Netherlands","active":true,"usgs":false}],"preferred":false,"id":750812,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sardans, Jordi","contributorId":210471,"corporation":false,"usgs":false,"family":"Sardans","given":"Jordi","email":"","affiliations":[],"preferred":false,"id":750813,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sigurdsson, Bjarni D.","contributorId":75857,"corporation":false,"usgs":true,"family":"Sigurdsson","given":"Bjarni","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":750814,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Van Sundert, Kevin","contributorId":210472,"corporation":false,"usgs":false,"family":"Van Sundert","given":"Kevin","email":"","affiliations":[],"preferred":false,"id":750815,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Wang, Ying-Ping","contributorId":210473,"corporation":false,"usgs":false,"family":"Wang","given":"Ying-Ping","email":"","affiliations":[],"preferred":false,"id":750816,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Zaehle, Sonke","contributorId":210474,"corporation":false,"usgs":false,"family":"Zaehle","given":"Sonke","affiliations":[],"preferred":false,"id":750817,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Ciais, Philippe 0000-0001-8560-4943","orcid":"https://orcid.org/0000-0001-8560-4943","contributorId":197934,"corporation":false,"usgs":false,"family":"Ciais","given":"Philippe","email":"","affiliations":[{"id":35082,"text":"LSCE, CEA CNRS UVSQ IPSL, Université Paris Saclay, 91191 Gif sur Yvette, France","active":true,"usgs":false}],"preferred":false,"id":750818,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70200806,"text":"70200806 - 2018 - Bank‐derived material dominates fluvial sediment in a suburban Chesapeake Bay watershed","interactions":[],"lastModifiedDate":"2018-11-02T14:00:32","indexId":"70200806","displayToPublicDate":"2018-11-02T14:00:27","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Bank‐derived material dominates fluvial sediment in a suburban Chesapeake Bay watershed","docAbstract":"Excess fine sediment is a leading cause of ecological degradation within the Chesapeake Bay watershed. To effectively target sediment mitigation measures, it is necessary to identify and quantify the delivery of sediment sources to local waterbodies.
This study examines the contributions of sediment sources within Upper Difficult Run, a suburbanized watershed in Fairfax County, Virginia. A source sediment library was constructed from stream banks, forest soils, and road dust. Target sediments were collected from fine channel deposits and suspended sediment during 16 storm events from 2008 to 2012. Apportionment of targets to sources was performed using Sed_SAT, a publicly available toolkit for sediment fingerprinting.
Bed sediment was dominated by stream bank material (mean: 98%), with minor contributions from forests (2%). Suspended fine sediments were also dominated by stream banks (suspended sediment concentration‐weighted mean: 91%), with minor contributions from roads (8%) and forests (<1%). Stream banks dominated at all discharges, and on the rising limb and at peak flow, sediment concentrations increased due to bank material rather than surface erosion.
Sediment budget data indicated that direct bank erosion was insufficient to account for the suspended load derived from stream banks. However, bank‐derived sediment re‐mobilized from in‐channel storage could account for this difference and, combined, resulted in a sediment delivery ratio of 0.847 for all bank‐derived sediments.
Results demonstrate that stream bank erosion is responsible for the majority of fine sediment in this suburban watershed of the Chesapeake Bay drainage area. Thus, management actions to control upland sources of sediment may have limited effect on the sediment conditions of Upper Difficult Run, whereas efforts focusing on bank stabilization, channel restoration, and/or stormwater management to reduce bank erosion may improve the ecological condition of these waterbodies.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3325","usgsCitation":"Cashman, M.J., Gellis, A.C., Gorman Sanisaca, L.E., Noe, G.E., Cogliandro, V., and Baker, A., 2018, Bank‐derived material dominates fluvial sediment in a suburban Chesapeake Bay watershed: River Research and Applications, v. 34, no. 8, p. 1032-1044, https://doi.org/10.1002/rra.3325.","productDescription":"13 p.","startPage":"1032","endPage":"1044","ipdsId":"IP-087831","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":359118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Upper Difficult Run","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.325,\n 38.8417\n ],\n [\n -77.3667,\n 38.8417\n ],\n [\n -77.3667,\n 38.8917\n ],\n [\n -77.325,\n 38.8917\n ],\n [\n -77.325,\n 38.8417\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"8","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-25","publicationStatus":"PW","scienceBaseUri":"5c10a8fce4b034bf6a7e4eca","contributors":{"authors":[{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":750621,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750622,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorman Sanisaca, Lillian E. 0000-0003-1711-3864","orcid":"https://orcid.org/0000-0003-1711-3864","contributorId":210381,"corporation":false,"usgs":true,"family":"Gorman Sanisaca","given":"Lillian","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":750624,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cogliandro, Vanessa","contributorId":210383,"corporation":false,"usgs":false,"family":"Cogliandro","given":"Vanessa","email":"","affiliations":[{"id":38109,"text":"Dipartimento di Agraria, Università degli Studi Mediterranea di Reggio Calabria, Feo di Vito, Reggio Calabria, Italy","active":true,"usgs":false}],"preferred":false,"id":750625,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baker, Anna 0000-0001-8194-7535 abaker@usgs.gov","orcid":"https://orcid.org/0000-0001-8194-7535","contributorId":210384,"corporation":false,"usgs":true,"family":"Baker","given":"Anna","email":"abaker@usgs.gov","affiliations":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":750626,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70199518,"text":"70199518 - 2018 - Monitoring wadeable stream habitat conditions in Southeast Coast Network parks: Protocol narrative","interactions":[],"lastModifiedDate":"2018-11-16T17:24:25","indexId":"70199518","displayToPublicDate":"2018-11-01T17:24:16","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/SECN/NRR—2018/1715 ","title":"Monitoring wadeable stream habitat conditions in Southeast Coast Network parks: Protocol narrative","docAbstract":"The Southeast Coast Network (SECN) has initiated a monitoring effort to assess habitat conditions in wadeable streams at national parks, recreation areas, battlefields, and monuments in Alabama, Georgia, and South Carolina. This monitoring effort includes Chattahoochee River National Recreation Area, Kennesaw Mountain National Battlefield Park, Congaree National Park, Horseshoe Bend National Military Park, and Ocmulgee National Monument.
Stream habitat monitoring was implemented in 2016, and focuses specifically on providing relevant data to assess the physical condition of Piedmont and upper Coastal Plain streams with respect to aquatic and riparian habitats and how these habitats may be changing over time. The habitat assessment methods proposed in this protocol rely on standard data collection methods and standard operating procedures currently in use by the U.S. Geological Survey, U.S. Environmental Protection Agency, and U.S. Forest Service that have been modified to better meet the needs of National Park Service (NPS) managers.
The Southeast Coast Network’s wadeable stream protocol was developed to begin a monitoring program that will provide insight into the status of, and trends in, stream and riparian habitat conditions. The number of reaches surveyed at each park is dependent on the spatial extent of the park and the total number of wadeable streams that are present within park boundaries. Regardless of the size of the park and the number of reaches that are to be monitored, selected reaches (1) are representative of the processes influencing the streams in each park; (2) can address current and anticipated management concerns, and (3) offer the most utility for future complementary studies.
","language":"English","publisher":"National Park Service","publisherLocation":"Fort Collins, CO","usgsCitation":"McDonald, J.M., Gregory, M., Riley, J.W., and Starkey, E.N., 2018, Monitoring wadeable stream habitat conditions in Southeast Coast Network parks: Protocol narrative: Natural Resource Report NPS/SECN/NRR—2018/1715 , xiii, 103 p.","productDescription":"xiii, 103 p.","ipdsId":"IP-066204","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":359534,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":357534,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2254874"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.890625,\n 29.49698759653577\n ],\n [\n -75.34423828125,\n 29.49698759653577\n ],\n [\n -75.34423828125,\n 36.56260003738545\n ],\n [\n -87.890625,\n 36.56260003738545\n ],\n [\n -87.890625,\n 29.49698759653577\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5befe5bae4b045bfcadf7f2e","contributors":{"authors":[{"text":"McDonald, Jacob M.","contributorId":208029,"corporation":false,"usgs":false,"family":"McDonald","given":"Jacob","email":"","middleInitial":"M.","affiliations":[{"id":37679,"text":"National Park Service Southeast Coast Inventory and Monitoring Unit","active":true,"usgs":false}],"preferred":false,"id":745742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gregory, Mark B.","contributorId":151024,"corporation":false,"usgs":false,"family":"Gregory","given":"Mark B.","affiliations":[],"preferred":false,"id":745741,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Riley, Jeffrey W. 0000-0001-5525-3134 jriley@usgs.gov","orcid":"https://orcid.org/0000-0001-5525-3134","contributorId":3605,"corporation":false,"usgs":true,"family":"Riley","given":"Jeffrey","email":"jriley@usgs.gov","middleInitial":"W.","affiliations":[{"id":316,"text":"Georgia Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":745740,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Starkey, Eric N.","contributorId":208030,"corporation":false,"usgs":false,"family":"Starkey","given":"Eric","email":"","middleInitial":"N.","affiliations":[{"id":37679,"text":"National Park Service Southeast Coast Inventory and Monitoring Unit","active":true,"usgs":false}],"preferred":false,"id":745743,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70197649,"text":"ofr20181098 - 2018 - Methods used for the collection and analysis of chemical and biological data for the Tapwater Exposure Study, United States, 2016–17","interactions":[],"lastModifiedDate":"2021-06-01T14:35:54.364321","indexId":"ofr20181098","displayToPublicDate":"2018-11-01T17:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1098","title":"Methods used for the collection and analysis of chemical and biological data for the Tapwater Exposure Study, United States, 2016–17","docAbstract":"In 2016, the U.S. Geological Survey (USGS) Environmental Health Mission Area, initiated the Tapwater Exposure Study as part of an infrastructure project to assess human exposure to potential threats from complex mixtures of contaminants. In the pilot phase (2016), samples were collected from 11 States throughout the United States, and in the second phase (2017), the study focused on the Greater Chicago area, including North and South Chicago, Illinois, and East Chicago, Indiana. Residential tapwater samples were collected at private residences during both phases, and during the first phase, samples were collected from Federal office buildings and from one office 19-liter water-bottle source. During the second phase, raw intake and treated (pre-distributional) water samples also were collected from four drinking-water treatment facilities in the Greater Chicago area. Samples were sent to laboratories at the USGS, U.S. Environmental Protection Agency, National Institute of Environmental Health Sciences, and Colorado School of Mines Center for Environmental Risk Assessment, for potential drinking-water pathogens, chemical, and bioassay analyses. These analyses included more than 400 chemicals such as trace elements, steroid hormones, pharmaceuticals, volatile organic compounds, pesticides, per- and polyfluorinated alkyl substances, cyanotoxins, and other organic compounds. The in vitro bioassay analyses included estrogen, androgen, and glucocorticoid receptor activity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181098","collaboration":"Prepared in cooperation with the Colorado School of Mines, Center for Environmental Risk Assessment; National Institutes of Health/National Institute of Environmental Health Sciences (NIH/NIEHS), National Toxicology Program Laboratory; University of Illinois at Chicago, School of Public Health; U.S. Environmental Protection Agency, National Exposure Research Laboratory; U.S. Environmental Protection Agency, National Health and Environmental Effects Laboratory","usgsCitation":"Romanok, K.M., Kolpin, D.W., Meppelink, S.M., Argos, M., Brown, J.B., DeVito, M.J., Dietze, J.E., Givens, C.E., Gray, J.L., Higgins, C.P., Hladik, M.L., Iwanowicz, L.R., Loftin, K.A., McCleskey, R.B., McDonough, C.A., Meyer, M.T., Strynar, M.J., Weis, C.P., Wilson, V.S., and Bradley, P.M., 2018, Methods used for the collection and analysis of chemical and biological data for the Tapwater Exposure Study, United States, 2016–17: U.S. Geological Survey Open-File Report 2018–1098, 79 p., https://doi.org/10.3133/ofr20181098.","productDescription":"Report: ix, 79 p.; 2 Data Releases","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094498","costCenters":[{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":358920,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1098/coverthb.jpg"},{"id":358921,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1098/ofr20181098.pdf","text":"Report","size":"1.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1098"},{"id":358923,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181071","text":"Open-File Report 2018-1071","linkHelpText":"- Concentrations of Lead and Other Inorganic Constituents in Samples of Raw Intake and Treated Drinking Water From the Municipal Water Filtration Plant and Residential Tapwater in Chicago, Illinois, and East Chicago, Indiana, July–December 2017"},{"id":358922,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7959GVJ","text":"USGS data release","description":"USGS data release","linkHelpText":"Target-Chemical Concentrations, Exposure Activity Ratios, and Bioassay Results for Assessment of Mixed-Organic/Inorganic-Chemical Exposure in USA Tapwater, 2016"},{"id":359038,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9NN0","text":"USGS data release","description":"USGS data release","linkHelpText":"Occurrence and Concentrations of Trace Elements in Discrete Tapwater Samples Collected in Chicago, Illinois and East Chicago, Indiana, 2017"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
The U.S. Geological Survey (USGS) Environmental Health Mission Area (EHMA) is providing comprehensive science on sources, movement, and transformation of contaminants and pathogens in watershed and aquifer drinking-water supplies and in built water and wastewater infrastructure (referred to as the USGS Water and Wastewater Infrastructure project) in the Greater Chicago Area and elsewhere in the United States, to fill data gaps identified by stakeholders and collaborators in drinking water and public health. EHMA Water and Wastewater Infrastructure research specifically provides insight into natural factors in the environment as well as those water-infrastructure components and processes (such as source-water corrosivity, treatment, plumbing, and so forth) that might influence human exposure to chemical and microbial contaminants at the residential tap. This infrastructure-exposure research role is fulfilled uniquely by the USGS and not by the U.S. Environmental Protection Agency (EPA), other agencies, or municipalities that focus on regulatory and policy activities and related compliance. The USGS approach to assessing the possible links between human health and chemical contaminant and pathogen exposure in drinking water is conducted in collaboration with public health experts and includes comprehensive characterization of the presence/absence and concentrations of more than 500 organic and 27 inorganic chemical constituents at the point of use (tap).
Laboratory results for lead and other inorganic contaminants in Chicago, Illinois, and East Chicago, Indiana, residential tapwater are being released to ensure the timely release of quality-assured data to participants in the study. Concentrations of lead and other inorganic constituents were assessed in drinking water at the point of use (kitchen tap or filter) in 45 residential locations and in two locations within each of the two Chicago water purification plants and the two East Chicago water filtration plants during July–December 2017. Three methods were used for analyzing lead. The most sensitive method had a reporting limit of 0.020 micrograms per liter (µg/L). When using the most sensitive analytical method, lead was detected in 39 of 45 residential tapwater samples, with concentrations ranging from less than 0.020 µg/L to 5.31 µg/L (median of the detected values = 0.481 µg/L). Concentrations of lead also were detected in Lake Michigan intake water at all water purification/filtration plant facilities at concentrations ranging from 0.083 to 0.330 µg/L, but were not detected above the reporting limit in any samples of treated, pre-distribution drinking water at any of the water purification/filtration plant facilities.
Because the USGS Water and Wastewater Infrastructure project in the Greater Chicago Area is focused on the potential human exposure to a broad suite of organic and inorganic contaminants in drinking water and is not focused specifically on lead, the sampling protocol did not include “first-draw,” stagnant sampling and samples were collected with point-of-use treatment in place, if present. Thus, the lead results reported herein are not appropriate for assessment of compliance with the EPA 1991 Lead and Copper Rule. Information resources for lead mitigation and water filtration are provided.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181071","collaboration":"Prepared in cooperation with the City of Chicago, Department of Water Management; City of East Chicago, Utilities Department; Indiana Department of Environmental Management, Drinking Water Branch; National Institutes of Health/National Institute of Environmental Health Sciences (NIH/NIEHS); University of Illinois at Chicago, School of Public Health","usgsCitation":"Romanok, K.M., Kolpin, D.W., Meppelink, S.M., Focazio, M.J., Argos, M., Hollingsworth, M.E., McCleskey, R.B., Putz, A.R., Stark, A., Weis, C.P., Zehraoui, A., and Bradley, P.M., 2018, Concentrations of lead and other inorganic constituents in samples of raw intake and treated drinking water from the municipal water filtration plant and residential tapwater in Chicago, Illinois, and East Chicago, Indiana, July–December 2017: U.S. Geological Survey Open-File Report 2018–1071, 10 p., https://doi.org/10.3133/ofr20181071.","productDescription":"Report: iv, 10 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094493","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":358915,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20181098","text":"Open-File Report 2018–1098","linkHelpText":"- Methods Used for the Collection and Analysis of Chemical and Biological Data for the Tapwater Exposure Study, United States, 2016–17"},{"id":358912,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1071/coverthb.jpg"},{"id":358914,"rank":3,"type":{"id":30,"text":"Data Release"},"url":" https://doi.org/10.5066/F70R9NN0","text":"USGS data release ","description":"USGS data release ","linkHelpText":"Occurrence and Concentrations of Trace Elements in Discrete Tapwater Samples Collected in Chicago, Illinois and East Chicago, Indiana, 2017"},{"id":358913,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1071/ofr20181071.pdf","text":"Report","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1071"}],"country":"United States","state":"Illinois, Indiana","city":"Chicago, East Chicago","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
1. The total flux of carbon—which includes gaseous emissions, lateral flux, and burial—from inland waters across the conterminous United States (CONUS) and Alaska is 193 teragrams of carbon (Tg C) per year. The dominant pathway for carbon movement out of inland waters is the emission of carbon dioxide gas across water surfaces of streams, rivers, and lakes (110.1 Tg C per year), a flux not identified in the First State of the Carbon Cycle Report (SOCCR1; CCSP 2007). Second to gaseous emissions are the lateral fluxes of carbon through rivers to coastal environments (59.8 Tg C per year). Total carbon burial in lakes and reservoirs represents the smallest flux for CONUS and Alaska (22.5 Tg C per year) (medium confidence).
2. Based on estimates presented herein, the carbon flux from inland waters is now understood to be four times larger than estimates presented in SOCCR1. The total flux of carbon from inland waters across North America is estimated to be 507 Tg C per year based on a modeling approach that integrates high-resolution U.S. data and continental-scale estimates of water area, discharge, and carbon emissions. This estimate represents a weighted average of 24 grams of carbon per m2 per year of continental area exported and removed through inland waters in North America (low confidence).
3. Future research can address critical knowledge gaps and uncertainties related to inland water carbon fluxes. This chapter, for example, does not include methane emissions, which cannot be calculated as precisely as other carbon fluxes because of significant data gaps. Key to reducing uncertainties in estimated carbon fluxes is increased temporal resolution of carbon concentration and discharge sampling to provide better representations of storms and other extreme events for estimates of total inland water carbon fluxes. Improved spatial resolution of sampling also could potentially highlight anthropogenic influences on the quantity and quality of carbon fluxes in inland waters and provide information for land-use planning and management of water resources. Finally, uncertainties could likely be reduced if the community of scientists working in inland waters establishes and adopts standard measurement techniques and protocols similar to those maintained through collaborative efforts of the International Ocean Carbon Coordination Project and relevant governmental agencies from participating nations.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report","language":"English","publisher":"U.S. Global Change Research Program","publisherLocation":"Washington, D.C.","doi":"10.7930/SOCCR2.2018.Ch14","usgsCitation":"Butman, D.E., Striegl, R.G., Stackpoole, S.M., Del Giorgio, P., Prairie, Y., Pilcher, D., Raymond, P., Paz Pellat, F., and Alcocer, J., 2018, Inland waters, chap. of Second State of the Carbon Cycle Report (SOCCR2): A Sustained Assessment Report, p. 568-595, https://doi.org/10.7930/SOCCR2.2018.Ch14.","productDescription":"28 p.","startPage":"568","endPage":"595","ipdsId":"IP-084988","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":360064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c0b957ee4b0c53ecb2aca8a","contributors":{"editors":[{"text":"Cavallaro, N.","contributorId":211183,"corporation":false,"usgs":false,"family":"Cavallaro","given":"N.","email":"","affiliations":[],"preferred":false,"id":753366,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Shrestha, G.","contributorId":211184,"corporation":false,"usgs":false,"family":"Shrestha","given":"G.","email":"","affiliations":[],"preferred":false,"id":753367,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Birdsey, R.","contributorId":14670,"corporation":false,"usgs":true,"family":"Birdsey","given":"R.","email":"","affiliations":[],"preferred":false,"id":753368,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Mayes, M. A.","contributorId":211185,"corporation":false,"usgs":false,"family":"Mayes","given":"M.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":753369,"contributorType":{"id":2,"text":"Editors"},"rank":4},{"text":"Najjar, R.G.","contributorId":30063,"corporation":false,"usgs":true,"family":"Najjar","given":"R.G.","affiliations":[],"preferred":false,"id":753370,"contributorType":{"id":2,"text":"Editors"},"rank":5},{"text":"Reed, S.C.","contributorId":72166,"corporation":false,"usgs":true,"family":"Reed","given":"S.C.","email":"","affiliations":[],"preferred":false,"id":753371,"contributorType":{"id":2,"text":"Editors"},"rank":6},{"text":"Romero-Lankao, P.","contributorId":211186,"corporation":false,"usgs":false,"family":"Romero-Lankao","given":"P.","affiliations":[],"preferred":false,"id":753372,"contributorType":{"id":2,"text":"Editors"},"rank":7},{"text":"Zhu, Z.","contributorId":10898,"corporation":false,"usgs":true,"family":"Zhu","given":"Z.","email":"","affiliations":[],"preferred":false,"id":753373,"contributorType":{"id":2,"text":"Editors"},"rank":8}],"authors":[{"text":"Butman, David E.","contributorId":145535,"corporation":false,"usgs":false,"family":"Butman","given":"David","email":"","middleInitial":"E.","affiliations":[{"id":16142,"text":"School of Environmental and Forest Sciences & Environmental Engineering, University of Washington, Seattle","active":true,"usgs":false}],"preferred":false,"id":753336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","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":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":753337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stackpoole, Sarah M. 0000-0002-5876-4922 sstackpoole@usgs.gov","orcid":"https://orcid.org/0000-0002-5876-4922","contributorId":3784,"corporation":false,"usgs":true,"family":"Stackpoole","given":"Sarah","email":"sstackpoole@usgs.gov","middleInitial":"M.","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":true,"id":753335,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Del Giorgio, Paul","contributorId":211167,"corporation":false,"usgs":false,"family":"Del Giorgio","given":"Paul","email":"","affiliations":[{"id":38187,"text":"Université du Québec à Montréal, Quebec, Montreal, Canada","active":true,"usgs":false}],"preferred":false,"id":753338,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prairie, Yves 0000-0003-1210-992X","orcid":"https://orcid.org/0000-0003-1210-992X","contributorId":211169,"corporation":false,"usgs":false,"family":"Prairie","given":"Yves","email":"","affiliations":[{"id":38187,"text":"Université du Québec à Montréal, Quebec, Montreal, Canada","active":true,"usgs":false}],"preferred":false,"id":753340,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pilcher, Darren 0000-0002-0763-3236","orcid":"https://orcid.org/0000-0002-0763-3236","contributorId":211168,"corporation":false,"usgs":false,"family":"Pilcher","given":"Darren","email":"","affiliations":[{"id":38188,"text":"NOAA Pacific Marine Environmental Laboratory, Seattle, Washington, United States of America","active":true,"usgs":false}],"preferred":false,"id":753339,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Raymond, Peter","contributorId":200764,"corporation":false,"usgs":false,"family":"Raymond","given":"Peter","affiliations":[],"preferred":false,"id":753341,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Paz Pellat, Fernando","contributorId":211170,"corporation":false,"usgs":false,"family":"Paz Pellat","given":"Fernando","email":"","affiliations":[{"id":38189,"text":"Colegio de Postgraduados Montecillo, Montecillo, Mexico","active":true,"usgs":false}],"preferred":false,"id":753342,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Alcocer, Javier","contributorId":211171,"corporation":false,"usgs":false,"family":"Alcocer","given":"Javier","email":"","affiliations":[{"id":38190,"text":"Universidad Nacional Autonoma de Mexico, Mexico City, Mexico","active":true,"usgs":false}],"preferred":false,"id":753343,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70201431,"text":"70201431 - 2018 - Radium attenuation and mobilization in stream sediments following oil and gas wastewater disposal in western Pennsylvania","interactions":[],"lastModifiedDate":"2018-12-13T15:06:56","indexId":"70201431","displayToPublicDate":"2018-11-01T15:06:49","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Radium attenuation and mobilization in stream sediments following oil and gas wastewater disposal in western Pennsylvania","docAbstract":"Centralized waste treatment facilities (CWTs) in Pennsylvania discharged wastewater from conventional and unconventional oil and gas (O&G) wells into surface waters until 2011, when a voluntary request from the Pennsylvania Department of Environmental Protection (PA DEP) encouraged recycling rather than treating and discharging unconventional O&G wastewater. To determine the effect of this request on the occurrence of radium in streams, we sampled sediments at five CWTs that processed conventional O&G wastewater from 2011 to 2017 and compared results to published data. Despite the policy change in 2011 that reduced disposal of unconventional wastes (i.e., Marcellus) to surface water in Pennsylvania, the continued disposal of conventional O&G wastewater led to elevated radium activities in sediments at the point of discharge that were often hundreds of times higher than background. While these elevated activities were also present in downstream sediments (1.5× higher than background), the elimination of unconventional O&G wastewater disposal through the CWTs since 2011 decreased radium loading to the stream by approximately 95%.
Sequential extractions and geochemical modeling using PHREEQC indicate that radium likely co-precipitates with barite or barite-celestite solid solutions and accumulates in the sediment as treated O&G effluent enters the stream. Adsorption of “exchangeable” radium, barium, and strontium on hydrous iron and manganese oxide coatings on fine-grained stream sediments is an important radium sequestration mechanism further downstream that can decrease the cation concentrations and potential for radio-barite co-precipitation. Radium downstream of CWTs was more abundant and more available for dissolution and desorption than radium in upstream sediments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2018.10.011","usgsCitation":"Van Sice, K., Cravotta, C., McDevitt, B., Tasker, T.L., Landis, J.D., Puhr, J., and Warner, N.R., 2018, Radium attenuation and mobilization in stream sediments following oil and gas wastewater disposal in western Pennsylvania: Applied Geochemistry, v. 98, p. 393-403, https://doi.org/10.1016/j.apgeochem.2018.10.011.","productDescription":"11 p.","startPage":"393","endPage":"403","ipdsId":"IP-101262","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":468266,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2018.10.011","text":"Publisher Index Page"},{"id":360257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Pennsylvania","volume":"98","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5c137dd4e4b006c4f8514890","contributors":{"authors":[{"text":"Van Sice, Katherine","contributorId":211454,"corporation":false,"usgs":false,"family":"Van Sice","given":"Katherine","affiliations":[{"id":38248,"text":"Civil and Environmental Engineering Department, The Pennsylvania State University,","active":true,"usgs":false}],"preferred":false,"id":754123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":754122,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McDevitt, Bonnie","contributorId":211455,"corporation":false,"usgs":false,"family":"McDevitt","given":"Bonnie","affiliations":[{"id":38248,"text":"Civil and Environmental Engineering Department, The Pennsylvania State University,","active":true,"usgs":false}],"preferred":false,"id":754124,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tasker, Travis L.","contributorId":211456,"corporation":false,"usgs":false,"family":"Tasker","given":"Travis","email":"","middleInitial":"L.","affiliations":[{"id":38248,"text":"Civil and Environmental Engineering Department, The Pennsylvania State University,","active":true,"usgs":false}],"preferred":false,"id":754125,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landis, Joshua D.","contributorId":211459,"corporation":false,"usgs":false,"family":"Landis","given":"Joshua","email":"","middleInitial":"D.","affiliations":[{"id":38249,"text":"Department of Earth Sciences, Dartmouth College, Hanover, NH","active":true,"usgs":false}],"preferred":false,"id":754128,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Puhr, Johnna","contributorId":211457,"corporation":false,"usgs":false,"family":"Puhr","given":"Johnna","email":"","affiliations":[{"id":38248,"text":"Civil and Environmental Engineering Department, The Pennsylvania State University,","active":true,"usgs":false}],"preferred":false,"id":754126,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Warner, Nathaniel R.","contributorId":211458,"corporation":false,"usgs":false,"family":"Warner","given":"Nathaniel","email":"","middleInitial":"R.","affiliations":[{"id":38248,"text":"Civil and Environmental Engineering Department, The Pennsylvania State University,","active":true,"usgs":false}],"preferred":false,"id":754127,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70199979,"text":"70199979 - 2018 - Are changes in lower trophic levels limiting prey-fish biomass and production in Lake Michigan?","interactions":[],"lastModifiedDate":"2018-11-16T14:25:51","indexId":"70199979","displayToPublicDate":"2018-11-01T14:25:44","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":190,"text":"Miscellaneous Publication","active":false,"publicationSubtype":{"id":3}},"seriesNumber":"2018-01","title":"Are changes in lower trophic levels limiting prey-fish biomass and production in Lake Michigan?","docAbstract":"To improve understanding of how recent changes in lower trophic levels in Lake Michigan could be affecting prey-fish biomass and production, the Lake Michigan Committee (LMC) convened a Lower Trophic Level Task Group and provided several charges that are responded to in this report. First, we compiled a comprehensive summary of lower trophiclevel data in Lake Michigan, separating out nearshore versus offshore trends over time. Declining trends were prevalent in offshore time series for phosphorus, chlorophyll a, biomass of total crustacean zooplankton, biomass of herbivorous cladocerans, and density of Diporeia spp. In the nearshore, declining trends were evident only for biomass of cyclopoid copepods and density of Diporeia spp. Second, we hypothesized specific mechanisms by which changes in lower trophic levels could affect prey-fish biomass and production and described the degree of empirical support for each mechanism. The best-supported hypothesis was that declining invertebrate prey (especially Diporeia spp.) was responsible for declining growth of prey fish, especially over the last decade when competition for prey resources should otherwise have been lessened due to declining prey-fish densities. As a result, declining growth potentially limits the prey-fish biomass that could have been attained had growth been maintained at the levels that were achieved in the 1980s and earlier. Third, we prioritized several lower trophic-level indicators that fishery managers could use to better inform decision making. The top-ranked indicator was annual reporting of Alewife (Alosa pseudoharengus) condition. Fourth, we prioritized the key monitoring and research gaps that limit our current understanding of how lower trophic levels influence fish production. The highest-priority monitoring gap was coordinated sampling of the nearshore, which, if accomplished, would complement annual reporting on offshore sampling. The top-ranked knowledge gap was identifying bottlenecks that regulate fish recruitment, given that recent changes in zooplankton distribution and abundance could be suppressing survival of larval fish and, ultimately, the biomass and production of prey fish. We provided three specific recommendations for the LMC to consider as they seek to better incorporate lower trophiclevel changes into their management decision process: (1) implement a coordinated and standardized nearshore monitoring program, (2) encourage funding agencies to use our prioritized lists in their decision processes, and (3) foster the already improved dialogue between those researching lower trophic levels and those researching fisheries.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Bunnell, D.B., Carrick, H.J., Madenjian, C.P., Rutherford, E.S., Vanderploeg, H.A., Barbiero, R.P., Hinchey-Malloy, E., Pothoven, S.A., Riseng, C.M., Claramunt, R.M., Bootsma, H.A., Elgin, A., Rowe, M., Thomas, S., Turschak, B.A., Czesny, S.J., Pangle, K., and Warner, D.M., 2018, Are changes in lower trophic levels limiting prey-fish biomass and production in Lake Michigan?: Miscellaneous Publication 2018-01, 42 p.","productDescription":"42 p.","ipdsId":"IP-095261","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":359527,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":358217,"type":{"id":11,"text":"Document"},"url":"https://glfc.org/pubs/misc/2018-01.pdf"}],"otherGeospatial":"Lake Michigan","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5befe5bbe4b045bfcadf7f32","contributors":{"authors":[{"text":"Bunnell, David B. 0000-0003-3521-7747 dbunnell@usgs.gov","orcid":"https://orcid.org/0000-0003-3521-7747","contributorId":195888,"corporation":false,"usgs":true,"family":"Bunnell","given":"David","email":"dbunnell@usgs.gov","middleInitial":"B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":747577,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carrick, Hunter J.","contributorId":150479,"corporation":false,"usgs":false,"family":"Carrick","given":"Hunter","email":"","middleInitial":"J.","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":747578,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":747579,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rutherford, Edward S.","contributorId":175426,"corporation":false,"usgs":false,"family":"Rutherford","given":"Edward","email":"","middleInitial":"S.","affiliations":[{"id":12789,"text":"NOAA Great Lakes Environmental Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":747580,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vanderploeg, Henry A.","contributorId":195891,"corporation":false,"usgs":false,"family":"Vanderploeg","given":"Henry","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":747581,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Barbiero, Richard P.","contributorId":108342,"corporation":false,"usgs":true,"family":"Barbiero","given":"Richard","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":747582,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hinchey-Malloy, Elizabeth","contributorId":208533,"corporation":false,"usgs":false,"family":"Hinchey-Malloy","given":"Elizabeth","email":"","affiliations":[],"preferred":false,"id":747583,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pothoven, Steven A.","contributorId":92998,"corporation":false,"usgs":false,"family":"Pothoven","given":"Steven","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":747584,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Riseng, Catherine M.","contributorId":30144,"corporation":false,"usgs":true,"family":"Riseng","given":"Catherine","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":747585,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Claramunt, Randall M.","contributorId":190497,"corporation":false,"usgs":false,"family":"Claramunt","given":"Randall","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":747586,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bootsma, Harvey A.","contributorId":150498,"corporation":false,"usgs":false,"family":"Bootsma","given":"Harvey","email":"","middleInitial":"A.","affiliations":[{"id":18038,"text":"University of Wisconsin, Milwaukee","active":true,"usgs":false}],"preferred":false,"id":747587,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Elgin, Ashley","contributorId":208535,"corporation":false,"usgs":false,"family":"Elgin","given":"Ashley","affiliations":[],"preferred":false,"id":747588,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Rowe, Mark","contributorId":208536,"corporation":false,"usgs":false,"family":"Rowe","given":"Mark","affiliations":[],"preferred":false,"id":747589,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Thomas, Sara","contributorId":208537,"corporation":false,"usgs":false,"family":"Thomas","given":"Sara","affiliations":[],"preferred":false,"id":747590,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Turschak, Benjamin A.","contributorId":150497,"corporation":false,"usgs":false,"family":"Turschak","given":"Benjamin","email":"","middleInitial":"A.","affiliations":[{"id":18038,"text":"University of Wisconsin, Milwaukee","active":true,"usgs":false}],"preferred":true,"id":747591,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Czesny, Sergiusz J.","contributorId":138598,"corporation":false,"usgs":false,"family":"Czesny","given":"Sergiusz","email":"","middleInitial":"J.","affiliations":[{"id":12458,"text":"Illinois Natural History Survey, Lake Michigan Biological Station","active":true,"usgs":false}],"preferred":false,"id":747592,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Pangle, Kevin","contributorId":195637,"corporation":false,"usgs":false,"family":"Pangle","given":"Kevin","affiliations":[],"preferred":false,"id":747593,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Warner, David M. 0000-0003-4939-5368 dmwarner@usgs.gov","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":2986,"corporation":false,"usgs":true,"family":"Warner","given":"David","email":"dmwarner@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":747594,"contributorType":{"id":1,"text":"Authors"},"rank":18}]}} ,{"id":70200636,"text":"70200636 - 2018 - Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning","interactions":[],"lastModifiedDate":"2018-11-26T14:23:15","indexId":"70200636","displayToPublicDate":"2018-11-01T14:23:11","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning","docAbstract":"High-resolution (HR) digital elevation models (DEMs), such as those at resolutions of 1 and 3 meters, have increasingly become more widely available, along with lidar point cloud data. In a natural environment, a detailed surface water drainage network can be extracted from a HR DEM using flow-direction and flow-accumulation modeling. However, elevation details captured in HR DEMs, such as roads and overpasses, can form barriers that incorrectly alter flow accumulation models, and hinder the extraction of accurate surface water drainage networks. This study tests a deep learning approach to identify the intersections of roads and stream valleys, whereby valley channels can be burned through road embankments in a HR DEM for subsequent flow accumulation modeling, and proper natural drainage network extraction.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"International Society for Photogrammetry and Remote Sensing","doi":"10.5194/isprs-archives-XLII-4-597-2018","usgsCitation":"Stanislawski, L., Brockmeyer, T., and Shavers, E.J., 2018, Automated road breaching to enhance extraction of natural drainage networks from elevation models through deep learning, in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XLII-4, p. 597-601, https://doi.org/10.5194/isprs-archives-XLII-4-597-2018.","productDescription":"5 p.","startPage":"597","endPage":"601","ipdsId":"IP-099807","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":468269,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/isprs-archives-xlii-4-597-2018","text":"Publisher Index Page"},{"id":359672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"XLII-4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-19","publicationStatus":"PW","scienceBaseUri":"5bfd146ee4b0815414ca38f4","contributors":{"authors":[{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":210088,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":749787,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brockmeyer, Tyler","contributorId":210089,"corporation":false,"usgs":true,"family":"Brockmeyer","given":"Tyler","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":749788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":749789,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}