Director, Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
MS 415
12201 Sunrise Valley Drive
Reston, VA 20192
Volcanic plumes are complex environments composed of gases and ash particles, where chemical and physical processes occur at different temperature and compositional regimes. Commonly, soluble sulphate- and chloride-bearing salts are formed on ash as gases interact with ash surfaces. Exposure to respirable volcanic ash following an eruption is potentially a significant health concern. The impact of such gas-ash interactions on ash toxicity is wholly un-investigated. Here, we study, for the first time, whether the interaction of volcanic particles with sulphur dioxide (SO2) gas, and the resulting presence of sulphate salt deposits on particle surfaces, influences toxicity to the respiratory system, using an advanced in vitro approach.
To emplace surface sulphate salts on particles, via replication of the physicochemical reactions that occur between pristine ash surfaces and volcanic gas, analogue substrates (powdered synthetic volcanic glass and natural pumice) were exposed to SO2 at 500 °C, in a novel Advanced Gas-Ash Reactor, resulting in salt-laden particles. The solubility of surface salt deposits was then assessed by leaching in water and geochemical modelling. A human multicellular lung model was exposed to aerosolised salt-laden and pristine (salt-free) particles, and incubated for 24 h. Cell cultures were subsequently assessed for biological endpoints, including cytotoxicity (lactate dehydrogenase release), oxidative stress (oxidative stress-related gene expression; heme oxygenase 1 and NAD(P)H dehydrogenase [quinone] 1) and its (pro-)inflammatory response (tumour necrosis factor α, interleukin 8 and interleukin 1β at gene and protein levels).
In the lung cell model no significant effects were observed between the pristine and SO2-exposed particles, indicating that the surface salt deposits, and the underlying alterations to the substrate, do not cause acute adverse effects in vitro. Based on the leachate data, the majority of the sulphate salts from the ash surfaces are likely to dissolve in the lungs prior to cellular uptake.
The findings of this study indicate that interaction of volcanic ash with SO2 during ash generation and transport does not significantly affect the respiratory toxicity of volcanic ash in vitro. Therefore, sulphate salts are unlikely a dominant factor controlling variability in in vitro toxicity assessments observed during previous eruption response efforts.
Sustainability has been at the forefront of the environmental research agenda of the integrated anthroposphere, hydrosphere, and biosphere since the last century and will continue to be critically important for future environmental science. However, linking humans and the environment through effective policy remains a major challenge for sustainability research and practice. Here we address this gap using an agent-based model (ABM) for a coupled natural and human systems in the Smoky Hill River Watershed (SHRW), Kansas, USA. For this freshwater-dependent agricultural watershed with a highly variable flow regime influenced by human-induced land-use and climate change, we tested the support for an environmental policy designed to conserve and protect fish biodiversity in the SHRW. We develop a proof of concept interdisciplinary ABM that integrates field data on hydrology, ecology (fish richness), social-psychology (value-belief-norm) and economics, to simulate human agents' decisions to support environmental policy. The mechanism to link human behaviors to environmental changes is the social-psychological sequence identified by the value-belief-norm framework and is informed by hydrological and fish ecology models. Our results indicate that (1) cultural factors influence the decision to support the policy; (2) a mechanism modifying social-psychological factors can influence the decision-making process; (3) there is resistance to environmental policy in the SHRW, even under potentially extreme climate conditions; and (4) the best opportunities for policy acceptance were found immediately after extreme environmental events. The modeling approach presented herein explicitly links biophysical and social science has broad generality for sustainability problems.
Environmental drivers of population vital rates, such as temperature and precipitation, often vary at short time scales, and these fluctuations can have important impacts on population dynamics. However, relationships between survival and environmental conditions are typically modeled at coarse temporal scales, ignoring the role of daily environmental variation in survival. Our goal was to determine the importance of fine-scale temporal variation in survival to population dynamics of stream salmonids. We extended the Cormack–Jolly–Seber model to estimate daily survival rates from seasonal samples of individually marked brook trout (Salvelinus fontinalis) in a stream network. Daily variation in temperature and flow were strongly associated with survival, but relationships varied between juvenile and adult trout and among streams. In all streams, juveniles had higher mortality in warm, low-flow conditions, but in the two larger streams, cold, high-flow conditions also reduced juvenile survival. Adult survival decreased during low flows, particularly in the fall spawning period. Differing survival responses among stream network components to short-term environmental events created shifts in optimal location for maximum survival across life stages, seasons, and years.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2018-0191","usgsCitation":"Childress, E., Nislow, K., Whiteley, A.R., O’Donnell, M., and Letcher, B., 2019, Daily estimates reveal fine-scale temporal and spatial variation in fish survival across a stream network: Canadian Journal of Fisheries and Aquatic Sciences, v. 76, no. 8, p. 1446-1458, https://doi.org/10.1139/cjfas-2018-0191.","productDescription":"13 p.","startPage":"1446","endPage":"1458","ipdsId":"IP-098110","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":377086,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Childress, Evan S.","contributorId":214287,"corporation":false,"usgs":false,"family":"Childress","given":"Evan S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":794922,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nislow, Keith","contributorId":201434,"corporation":false,"usgs":false,"family":"Nislow","given":"Keith","affiliations":[{"id":27110,"text":"U.S. Dept of Agriculture, Forest Service","active":true,"usgs":false}],"preferred":false,"id":794925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whiteley, Andrew R.","contributorId":150155,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":794923,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Donnell, Matthew 0000-0002-9089-2377 mjodonnell@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-2377","contributorId":167315,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Matthew","email":"mjodonnell@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794924,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Letcher, Benjamin 0000-0003-0191-5678 bletcher@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-5678","contributorId":169305,"corporation":false,"usgs":true,"family":"Letcher","given":"Benjamin","email":"bletcher@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794926,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70212321,"text":"70212321 - 2019 - Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus)","interactions":[],"lastModifiedDate":"2020-08-14T14:34:26.531968","indexId":"70212321","displayToPublicDate":"2020-07-01T09:26:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus)","title":"Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus)","docAbstract":"American horseshoe crabs (Limulus polyphemus) have varied migration patterns and harvesting pressure throughout their range, potentially leading to regional differences in population dynamics. Here, a multi-state mark–recapture model was used to estimate annual survival and exchange rates of adult horseshoe crabs across three geographic regions in Long Island, NY (South Shore, North Shore, and Jamaica Bay areas). Under the New York Horseshoe Crab Monitoring program, a total of 22,525 adult horseshoe crabs were tagged and 879 (3.9%) unique recaptures were observed from 2007 to 2016. Model-averaged annual survival in the North Shore population was higher at 68% (95% confidence interval (CI) 61.9–73.4) when compared to the South Shore (56.8%, 95% CI 51.1–62.2) and Jamaica Bay (54.5%, 95% CI 47.0–61.7) regions. Differences in survival between the North Shore and South Shore may reflect the greater harvest pressure directed along the South Shore. Contrary to expectations for a primarily closed region, Jamaica Bay survival was low, but not attributable to reported harvest related activities. Annual movement from the Jamaica Bay into the adjacent South Shore region was 19.8% (95% CI 13.1–28.9), but annual exchange rates ranging from 0.5 to 5.0% were observed between other regions. For example, movement from the South Shore and North Shore into Jamaica Bay was 3.5% (95% CI 2.3–5.9) and 0.5% (95% CI 0.0–1.0), respectively. There was strong support for sex-specific differences in survival, primarily driven by the low survival of females in Jamaica Bay (33.8%, 95% CI 21.1–50.5). Our findings reveal potential management implications, such as regional survival differences within a uniformly managed stock, and net emigration from a predominantly closed to open harvest region reducing the effectiveness of a protected area.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-019-00595-1","usgsCitation":"Bopp, J.J., Sclafani, M., Smith, D.R., McKown, K., Sysak, R., and Cerrato, R., 2019, Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus): Estuaries and Coasts, v. 42, p. 1570-1585, https://doi.org/10.1007/s12237-019-00595-1.","productDescription":"16 p.","startPage":"1570","endPage":"1585","ipdsId":"IP-106088","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":377519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, New York","otherGeospatial":"Jamaica Bay, Long Island, North Shore, South Shore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.1302490234375,\n 40.06125658140474\n ],\n [\n -73.916015625,\n 40.027614437486655\n ],\n [\n -73.8775634765625,\n 40.49709237269567\n ],\n [\n -72.65808105468749,\n 40.66813955408042\n ],\n [\n -71.74072265625,\n 41.03793062246529\n ],\n [\n -71.9439697265625,\n 41.20345619205131\n ],\n [\n -72.333984375,\n 41.29431726315258\n ],\n [\n -72.894287109375,\n 41.261291493919884\n ],\n [\n -73.49853515625,\n 41.062786068733026\n ],\n [\n -73.8885498046875,\n 40.84290487729676\n ],\n [\n -74.0863037109375,\n 40.66397287638688\n ],\n [\n -74.080810546875,\n 40.576412521044425\n ],\n [\n -74.2730712890625,\n 40.49291502689579\n ],\n [\n -74.234619140625,\n 40.451127265872316\n ],\n [\n -74.0643310546875,\n 40.32141999593439\n ],\n [\n -74.1302490234375,\n 40.06125658140474\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2019-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Bopp, Justin J.","contributorId":238554,"corporation":false,"usgs":false,"family":"Bopp","given":"Justin","email":"","middleInitial":"J.","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":796384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sclafani, Matthew","contributorId":238556,"corporation":false,"usgs":false,"family":"Sclafani","given":"Matthew","email":"","affiliations":[{"id":47742,"text":"Cornell Cooperative Extension","active":true,"usgs":false}],"preferred":false,"id":796385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":796386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKown, Kim","contributorId":238557,"corporation":false,"usgs":false,"family":"McKown","given":"Kim","email":"","affiliations":[{"id":47744,"text":"New York Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":796387,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sysak, Rachel","contributorId":238558,"corporation":false,"usgs":false,"family":"Sysak","given":"Rachel","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":796388,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cerrato, Robert","contributorId":238559,"corporation":false,"usgs":false,"family":"Cerrato","given":"Robert","email":"","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":796389,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203838,"text":"70203838 - 2019 - Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture","interactions":[],"lastModifiedDate":"2020-05-29T19:13:50.212524","indexId":"70203838","displayToPublicDate":"2020-05-29T14:03:13","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5959,"text":"Wisconsin Geological and NaturalHistory Survey Bulletin","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"B112","title":"Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture","docAbstract":"A groundwater flow model for western Chippewa County, Wisconsin, was developed by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS) using the computer program MODFLOW. The model is the result of a five-year groundwater study commissioned by Chippewa County in 2012 to evaluate the effects of industrial sand mining and irrigated agriculture on the county’s water resources. The study incorporates existing data and newly acquired data from fieldwork conducted within the study area. The groundwater model may be useful for future investigations, such as evaluation of proposed high-capacity well sites, development of municipal wellhead protection plans, and studies that seek to further quantify surface water-groundwater relationships.
The model conceptualizes the hydrostratigraphy of western Chippewa County as six stacked layers. Each layer is distinct, beginning with unlithified glacial material at the surface, and alternating between sandstones (that act as aquifers) and shale units (that serve as aquitards). The model is bounded below by Precambrian crystalline bedrock and its perimeter was derived from a regional-scale groundwater flow model.
The MODFLOW model represented average conditions during 2011–2013 with “steady-state” assumptions, meaning that simulated water levels do not fluctuate seasonally or from year to year. Steady-state models simplify natural variability, making results of scenario simulations easier to interpret and compare while also maximizing effects of stressors because the simulated stress is always applied (not halted after a few months or years). Model calibration used the parameter estimation code (PEST), and calibration targets included heads (groundwater levels) and streamflows. Calibration focused on 2011–2013 because a large amount of head and streamflow data were available for that period.
The MODFLOW model explicitly simulates all sources and sinks of water, including groundwater/surface-water interaction with streamflow routing. Model input included estimates of aquifer hydraulic conductivity and a spatial groundwater recharge distribution developed using a GIS-based soil-water-balance (SWB) model applied to the model area. Groundwater withdrawals were simulated for 269 high-capacity wells across the entire model domain, which includes western Chippewa County and adjacent portions of Dunn, Barron, and Rusk Counties. Collectively, these wells withdrew about 1.14 million gallons per year between 2011 and 2013.
Once the model was calibrated, it was applied to two distinct scenarios of increased groundwater withdrawals: one evaluating hydrologic effects of more intensive industrial sand mining and the second evaluating the hydrologic effects of more intensive agricultural irrigation practices. Each scenario was developed with input from Chippewa County and a stakeholder group established expressly for this study. The scenarios were designed to represent reasonable future buildout conditions for both mining and irrigated agriculture. The mining scenario underscores the potential hydrologic effects related to changing land-use practices (i.e., hilltops and farmland becoming sand mines), while the irrigated agriculture scenario illustrates the potential hydrologic effects of intensifying existing land-use practices (i.e., installing new wells to irrigate farm fields).
While each scenario evaluated distinctly different conditions, modeling results demonstrated the potential of both scenarios to lower the water table and reduce baseflows in headwater streams within the modeled area. In the case of irrigated agriculture, hydrologic effects were associated directly with groundwater withdrawals. By assuming that irrigation did not decrease, this steady-state simulation represented a sustained future effect. By contrast, hydrologic effects of industrial sand mining were the result of both groundwater withdrawals at mines and land-use changes that effectively reduced recharge to groundwater over distinct phases of active mining. This scenario included a post-mining phase, during which groundwater withdrawals stopped and mined areas were reclaimed to undeveloped prairie grass cover. If reclamation to undeveloped prairie indeed occurs as simulated, long-term increases in the water table and stream baseflows are possible. In this sense, the scenario representing build out of irrigated agriculture led to long-term baseflow declines while the future buildout of industrial sand mining led to declines that dissipated following mine reclamation to undisturbed prairie.
Future investigations in similar hydrogeologic settings may find the following insights gleaned from this study useful:
❚❚ The characterization of hydrogeologic properties, delineation of hydrogeologic units, and calibration of groundwater flow models benefited from incorporation of accurate well construction reports, high-quality borehole geophysical logs, and streamflow gaging data.
❚❚ Infiltration testing performed in active mining areas provided evidence that reducing the degree and extent of compaction and enhancing areas designed to retain and infiltrate stormwater runoff could potentially reduce runoff and increase groundwater recharge.
❚❚ Similarly, reclaiming mined areas to prairie grasses would be expected to reduce runoff and increase groundwater recharge by reducing compaction and improving soil structure and vegetation that can slow runoff and enhance infiltration.
","language":"English","publisher":"Wisconsin Geological and Natural History Survey","usgsCitation":"Parsen, M., Juckem, P.F., Gotkowitz, M., and Fienen, M.N., 2019, Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture: Wisconsin Geological and NaturalHistory Survey Bulletin B112, 74 p.","productDescription":"74 p.","ipdsId":"IP-093476","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":375174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375173,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.wisc.edu/pubs/b112/"}],"country":"United States","state":"Wisconsin","county":"Chippewa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.8511962890625,\n 44.859762688042736\n ],\n [\n -91.31011962890625,\n 44.859762688042736\n ],\n [\n -91.31011962890625,\n 45.55060191034006\n ],\n [\n -91.8511962890625,\n 45.55060191034006\n ],\n [\n -91.8511962890625,\n 44.859762688042736\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parsen, Michael","contributorId":216283,"corporation":false,"usgs":false,"family":"Parsen","given":"Michael","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":764401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gotkowitz, Madeline","contributorId":216284,"corporation":false,"usgs":false,"family":"Gotkowitz","given":"Madeline","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":764402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":171511,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764403,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206903,"text":"sir20195136 - 2019 - Geohydrology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York","interactions":[],"lastModifiedDate":"2020-05-14T11:35:30.264583","indexId":"sir20195136","displayToPublicDate":"2020-05-13T16:20:00","publicationYear":"2019","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":"2019-5136","displayTitle":"Geohydrology and Water Quality of the Unconsolidated Aquifers in the Enfield Creek Valley, Town of Enfield, Tompkins County, New York","title":"Geohydrology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York","docAbstract":"From 2013 to 2018, the U.S. Geological Survey, in cooperation with the Town of Enfield and the Tompkins County Planning Department, studied the unconsolidated aquifer in the Enfield Creek Valley in the town of Enfield, Tompkins County, New York. The valley will likely undergo future development as the population of Tompkins County increases and spreads out from the metropolitan areas. The Town of Enfield, Tompkins County, and the New York State Departments of Health and Environmental Conservation need geohydrologic information to help planners develop a more comprehensive approach to water-resource management in Tompkins County.
The Enfield Creek Valley is underlain by an unconfined aquifer that consists of saturated alluvium, alluvial-fan deposits, and ice-contact (kame) sand and gravel. A confined aquifer of discontinuous ice-contact sand and gravel overlies bedrock. Depth to bedrock in the valley ranges from about 50 feet below land surface from just north of the Enfield Creek divide in the northern part of the aquifer to the confluence of Fivemile Creek to at least 140 feet below land surface from Fivemile Creek to where the valley orientation changes from north-south to northwest-southeast. Depth to bedrock is much shallower from the valley orientation change to the southeastern part of the aquifer because Enfield Creek has carved through overlying sediments into bedrock as the creek drops 450 feet into the Cayuga Inlet Valley. A small buried valley running south to north was identified within the Fivemile Creek drainage along the western edge of the town. However, the valley fill consists of glacial till, and no sand-and-gravel aquifer is present.
The unconfined aquifers are recharged by direct infiltration of precipitation, surface runoff, and shallow subsurface flow from hillsides, and by seepage loss from streams overlying the aquifer. The confined aquifers are recharged mostly by precipitation that enters the adjacent valley walls, by groundwater flowing from bordering till or bedrock, and by flow from the bottom of the valley. Also, some recharge may be occurring where confining units are absent or from confining units with sediments of moderate permeability.
Groundwater discharges to Enfield Creek, its tributaries, and wetlands and is lost through evapotranspiration from the water table or is withdrawn from domestic, commercial, and agricultural wells. About 700 individual well owners depend on the unconsolidated aquifers for their water supply. An estimated 28,300,000 gallons per year are withdrawn.
Groundwater samples were collected from eight test wells drilled for this study, and six surface-water samples were collected from five locations on Enfield Creek. Of the eight wells sampled, two were finished in unconfined sand-and-gravel aquifers, two were finished in confined sand-and-gravel aquifers, and four were finished at or near the shale bedrock surface.
Water quality in the study area generally met State and Federal drinking-water standards. However, some samples exceeded maximum contaminant levels for barium (25 percent of samples) and secondary maximum containment levels for chloride (25 percent), dissolved solids (25 percent of samples), iron (70 percent of samples), and manganese (75 percent of samples). Groundwater from 75 percent of the wells sampled for methane had concentrations greater than the Office of Surface Mining Reclamation and Enforcement recommended action level of 10 to 28 milligrams per liter. The two deepest wells sampled, TM1075 and TM1077, had the highest specific conductance, chloride, and sodium concentrations of all wells sampled. The chloride/bromide ratios of these samples suggest the source may represent a mixture of saline formation waters with shallow dilute groundwater and may receive recharge contribution from two tributaries overlying bedrock to the west and southwest of the aquifer. In general, the highest yields are from wells completed within about 50 feet below land surface, which may tap either type of aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195136","collaboration":"Prepared in cooperation with the Town of Enfield and the Tompkins County Planning Department","usgsCitation":"Fisher, B.N., Heisig, P.M., and Kappel, W.M., 2019, Geohydrology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, Town of Enfield, Tompkins County, New York (ver. 1.1, May 2020): U.S. Geological Survey Scientific Investigations Report 2019–5136, 52 p., https://doi.org/10.3133/sir20195136.","productDescription":"Report: vi, 52 p.; 3 Data Releases","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103465","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":370147,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZFTDFY","text":"USGS data release","linkHelpText":"A horizontal-to-vertical spectral ratio soundings and depth-to-bedrock data for geohydrology and water quality investigation of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York, April 2013–August 2015"},{"id":370148,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90FOTQV","text":"USGS data release","linkHelpText":"Records of selected wells for geohydrology and water quality investigation of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York, 2013–2018"},{"id":374790,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5136/coverthb2.jpg"},{"id":374788,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T3U63J","text":"USGS data release","linkHelpText":"Geospatial datasets for the hydrogeology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, Town of Enfield, Tompkins County, New York"},{"id":374789,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5136/versionhist.txt","text":"Version history","size":"1.35 KB","linkFileType":{"id":2,"text":"txt"}},{"id":374792,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5136/sir20195136.pdf","text":"Report","size":"5.95 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"New York","county":"Tompkins County","otherGeospatial":"Enfield Creek Valley, Town of Enfield","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.69281005859375,\n 42.47817430242155\n ],\n [\n -76.68731689453125,\n 42.39050147746088\n ],\n [\n -76.5692138671875,\n 42.39405131362432\n ],\n [\n -76.57230377197266,\n 42.48222557002593\n ],\n [\n -76.69281005859375,\n 42.47817430242155\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: May 13, 2020; Version 1.0 December 13, 2019","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Multi-species approaches to wildlife management have become commonplace and purport to benefit entire biological communities. These strategies aim to manage different, often taxonomically distant species under a single regime based on shared habitat associations and/or co-occurrence in the landscape. We tested the efficacy of multi-species management in the context of creating and maintaining early-successional forest cover types using two species of migratory birds that breed in eastern North America and are each the focus of intensive, concurrent, and overlapping management. American woodcock (Scolopax minor) and golden-winged warblers (Vermivora chrysoptera) breed in similar diverse-forest landscapes. Each species purportedly benefits from management for the other species and both are often used as flagship species for the creation of young forest and the conservation of associated avian communities. However, the landscape-species relationships that drive reproductive success and population stability in these species have not been explicitly compared. Here, we use previously published spatially-explicit models of productivity (the number of juveniles raised to a biologically significant milestone) to identify the relationship(s) between productivity of American woodcock and golden-winged warblers across a shared landscape. We found productivity to be negatively associated between these species on the same landscape at all spatial scales we modelled (1 m2–100 ha). Our results suggest that, with regards to productivity, American woodcock and golden-winged warblers have opposing relationships with the composition of the landscapes in which they coexist and therefore should not be assumed to benefit similarly from any individual management action at any relevant spatial scale.
We estimated detection probabilities of bird carcasses along sandy beaches and in marsh edge habitats in the northern Gulf of Mexico to help inform models of bird mortality associated with the Deepwater Horizon oil spill. We also explored factors that may influence detection probability, such as carcass size, amount of scavenging, location on the beach, habitat type, and distance into the marsh. Detection probability for medium-sized carcasses (200–500 g) ranged from 0.82 (SE = 0.09) to 0.93 (SE = 0.04) along sandy beaches. Within sandy beaches, we found that intact/slightly scavenged carcasses were easier to detect than heavily scavenged ones and did not find strong effects of location on the beach on detection probability. We estimated detection rate for each combination of scavenging state, carcass size, and position along sandy beaches. In marsh edge habitats, detection ranged from 0.04 (SE = 0.04) to 0.86 (SE = 0.10), with detection rates rapidly increasing from small (< 200 g) to medium carcass sizes and leveling off between medium and extra-large (> 1000 g) carcasses regardless of vegetation type (Spartina or Phragmites). Carcasses of all sizes were generally harder to locate in Spartina-dominated marshes than in Phragmites-dominated ones. A subset of the data for which we could adequately assess the effect of distance into the marsh indicated that detection rates generally declined the farther a carcass was into marsh vegetation. Based on power analyses, our ability to identify predictors that influence detection rates would be higher with larger numbers of carcasses, greater numbers of search trials per carcass, or more balanced sampling distributions across predictor values.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10661-019-7924-z","usgsCitation":"Zimmerman, G.S., Varela, V., and Yee, J.L., 2019, Detection probabilities of bird carcasses along sandy beaches and marsh edges in the northern Gulf of Mexico: Environmental Monitoring and Assessment, v. 191, no. suppl 4, 816, 15 p., https://doi.org/10.1007/s10661-019-7924-z.","productDescription":"816, 15 p.","ipdsId":"IP-094446","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458847,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10661-019-7924-z","text":"Publisher Index Page"},{"id":375518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.2352294921875,\n 29.544787796199465\n ],\n [\n -94.0814208984375,\n 29.640320395351402\n ],\n [\n -93.61450195312499,\n 29.72145191669099\n ],\n [\n -93.218994140625,\n 29.754839972510933\n ],\n [\n -92.5213623046875,\n 29.559123451577964\n ],\n [\n -92.2247314453125,\n 29.501768632523262\n ],\n [\n -92.08740234375,\n 29.559123451577964\n ],\n [\n -91.8841552734375,\n 29.482643134466617\n ],\n [\n -91.1810302734375,\n 29.204918463909035\n ],\n [\n -90.5438232421875,\n 29.16655229520015\n ],\n [\n -90.3076171875,\n 29.28160772298835\n ],\n [\n -90.2691650390625,\n 29.017748018496047\n ],\n [\n -89.9395751953125,\n 29.176145182559758\n ],\n [\n -89.70886230468749,\n 29.3295093083647\n ],\n [\n -89.132080078125,\n 28.93124697186731\n ],\n [\n -88.978271484375,\n 29.257648503615542\n ],\n [\n -89.1485595703125,\n 30.12612436422458\n ],\n [\n -89.1925048828125,\n 30.311245603935003\n ],\n [\n -88.65966796875,\n 30.306503259848835\n ],\n [\n -88.3245849609375,\n 30.372875188118016\n ],\n [\n -88.1927490234375,\n 30.306503259848835\n ],\n [\n -87.77526855468749,\n 30.14512718337613\n ],\n [\n -87.3797607421875,\n 30.292274851024256\n ],\n [\n -86.517333984375,\n 30.358656420788783\n ],\n [\n -85.8251953125,\n 30.183121842195515\n ],\n [\n -85.45166015624999,\n 29.935895213372444\n ],\n [\n -85.3143310546875,\n 30.050076521698735\n ],\n [\n -85.7977294921875,\n 30.37761431777479\n ],\n [\n -86.3031005859375,\n 30.519681272749402\n ],\n [\n -87.0172119140625,\n 30.62845887475364\n ],\n [\n -87.4566650390625,\n 30.519681272749402\n ],\n [\n -88.39050292968749,\n 30.50548389892728\n ],\n [\n -89.1925048828125,\n 30.48181700282728\n ],\n [\n -89.62646484375,\n 30.330212685432734\n ],\n [\n -90.362548828125,\n 29.859701442126756\n ],\n [\n -90.68115234375,\n 29.506549442788593\n ],\n [\n -91.2579345703125,\n 29.654642479663647\n ],\n [\n -91.7962646484375,\n 29.92637417863576\n ],\n [\n -92.30712890625,\n 29.78821690967894\n ],\n [\n -93.3673095703125,\n 29.88351825335318\n ],\n [\n -94.47143554687499,\n 29.716681287231072\n ],\n [\n -94.2352294921875,\n 29.544787796199465\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"191","issue":"suppl 4","noUsgsAuthors":false,"publicationDate":"2020-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":790710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Varela, Veronica","contributorId":225184,"corporation":false,"usgs":false,"family":"Varela","given":"Veronica","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":790711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":790712,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70205999,"text":"sir20195118 - 2019 - Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the northeastern United States","interactions":[],"lastModifiedDate":"2020-02-04T06:08:18","indexId":"sir20195118","displayToPublicDate":"2020-02-04T07:20:00","publicationYear":"2019","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":"2019-5118","displayTitle":"Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States","title":"Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the northeastern United States","docAbstract":"SPAtially Referenced Regression On Watershed attributes (SPARROW) models were developed to quantify and improve the understanding of the sources, fate, and transport of nitrogen, phosphorus, and suspended sediment in the northeastern United States. Excessive nutrients and suspended sediment from upland watersheds and tributary streams have contributed to ecological and economic degradation of northeastern surface waters. Recent efforts to reduce the flux of nutrients and suspended sediment in northeastern streams and to downstream estuaries have met with mixed results, and expected ecological improvements have been observed in some areas but not in others. Effective watershed management and restoration to improve surface-water quality are complicated by the multitude of nutrient sources in the Northeast and the multitude of natural and human landscape processes affecting the delivery of nutrients and suspended sediment from upland areas to and within surface waters. Individual models were constructed representing streamflow and the loads of total nitrogen, total phosphorus, and suspended sediment from watersheds draining to the Atlantic Ocean from southern Virginia through Maine.
Northeastern streams contribute 303,000 metric tons (t) of nitrogen, 25,300 t of phosphorus, and 14,700,000 t of suspended sediment, annually (on average), to waters along the Atlantic Coast of North America. Although atmospheric deposition and natural mineral erosion contribute to nitrogen and phosphorus loads, respectively, in northeastern streams, most of the contributions are attributable to urban or agricultural sources. Within the Northeast, average yields of nutrients are therefore generally greater from densely populated or intensively cultivated areas of the mid-Atlantic region, the Hudson, Mohawk, and Connecticut River valleys, and the coastal areas of southern New England than in predominantly forested areas such as northern New England. Average upland sediment yields are similarly greater from agricultural areas than from urban or forested areas and are therefore generally greatest in areas yielding the greatest nutrients. Landscape conditions that are significant to nitrogen delivery from uplands to streams likely reflect the importance of groundwater transport in carbonate settings and of denitrification for removing nitrogen from uplands. Nitrogen losses to streams in agricultural areas are apparently mitigated by the use of cover crops but are exacerbated by the use of conservation tillage or no-till practices. The transport of phosphorus and suspended sediment from uplands to streams is greater in areas of more erodible soils but mitigated in agricultural areas with greater use of conservation tillage or no-till practices. Loads of nutrients and suspended sediment are significantly reduced within the stream network in impounded reaches, and nitrogen load is also significantly reduced in small flowing reaches.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195118","collaboration":"National Water Quality Program","usgsCitation":"Ator, S.W., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Northeastern United States: U.S. Geological Survey Scientific Investigations Report 2019–5118, 57 p., https://doi.org/10.3133/sir20195118.","productDescription":"Report: ix, 57 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103253","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":371972,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5118/sir20195118.pdf","text":"Report","size":"31.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5118"},{"id":370715,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370717,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":370376,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-northeast-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370375,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NKNVQO","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Northeastern United States, 2012 base year"},{"id":370917,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5118/coverthb4.jpg"},{"id":370716,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370718,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"}],"otherGeospatial":"Northeastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.81640625,\n 37.43997405227057\n ],\n [\n -75.05859375,\n 37.37015718405753\n ],\n [\n -73.564453125,\n 40.38002840251183\n ],\n [\n -71.455078125,\n 40.64730356252251\n ],\n [\n -70.048828125,\n 42.032974332441405\n ],\n [\n -70.48828125,\n 43.32517767999296\n ],\n [\n -69.169921875,\n 43.77109381775651\n ],\n [\n -66.884765625,\n 44.77793589631623\n ],\n [\n -68.02734375,\n 47.21956811231547\n ],\n [\n -69.43359375,\n 47.338822694822\n ],\n [\n -70.13671875,\n 45.521743896993634\n ],\n [\n -74.970703125,\n 44.84029065139799\n ],\n [\n -77.080078125,\n 43.32517767999296\n ],\n [\n -79.8046875,\n 40.38002840251183\n ],\n [\n -82.001953125,\n 38.61687046392973\n ],\n [\n -76.81640625,\n 37.43997405227057\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Spatially Referenced Regression On Watershed attributes (SPARROW) models were applied to describe and estimate mean-annual streamflow and transport of total nitrogen (TN), total phosphorus (TP), and suspended sediment (SS) in streams and delivered to coastal waters of the southeastern United States on the basis of inputs and management practices centered near 2012, the base year of the model. Previously published TN and TP models for 2002 served as a starting point and reference for comparison. The datasets developed for the 2012 models not only represent updates of previous conditions but also incorporate new approaches for characterizing sources and transport processes that were not available for previous models.
Variability in streamflow across the southeastern United States was explained as a function of precipitation adjusted for evapotranspiration, spring discharge, and municipal and domestic wastewater discharges to streams. Results from the streamflow model were used as input to the water-quality SPARROW models, and areas with large streamflow prediction errors—urban areas and karst areas—were used to provide guidance on where additional data are needed to improve routing of flow.
Variability in TN transport in Southeast streams was explained by the following five sources in order of decreasing mass contribution to streams: atmospheric deposition, agricultural fertilizer, municipal wastewater, manure from livestock, and urban land. Variable rates of TN delivery from source to stream were attributed to variation among catchments in climate, soil texture, and vegetative cover, including the extent of cover crops in the watershed. Variability in TP transport in Southeast streams was explained by the following six sources in order of decreasing mass contribution to streams: parent-rock minerals, urban land, manure from livestock, municipal wastewater, agricultural fertilizer, and phosphate mining. Varying rates of TP delivery were attributed to variation in climate, soil erodibility, depth to water table, and the extent of conservation tillage practices in the watershed.
Variability in SS transport in Southeast streams was explained by variable sediment export rates for different combinations of land cover and geologic setting (for upland sources of sediment) and by gains in stream power caused by longitudinal changes in channel hydraulics (for channel sources of sediment). Sediment yields for the transitional land cover (shrub, scrub, herbaceous, and barren) varied widely depending on geologic setting and on agricultural land cover. Varying rates of SS delivery, like those for TP, were attributed to variation in climate, soil erodibility, and the extent of conservation tillage practices in the watershed, as well as to areal extent of canopy land cover in the 100-meter buffer along the channel. Relatively large uncertainty, compared to the other three models, for almost all the SS source coefficients indicates the need for caution when interpreting the results from the sediment model.
TN, TP, and SS inputs to streams from sources were balanced in the models with losses from physical processes in streams and reservoirs and with water withdrawals. The losses in streams and reservoirs along with withdrawals removed 35, 44, and 65 percent of the TN, TP, and SS load, respectively, that entered streams before reaching coastal waters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195135","collaboration":"National Water Quality Program","usgsCitation":"Hoos, A.B., and Roland, V.L. II, 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in the Southeastern United States: U.S. Geological Survey Scientific Investigations Report 2019–5135, 91 p., https://doi.org/10.3133/sir20195135.","productDescription":"Report: xi, 87 p.; Data Release; HTML","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101532","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":370725,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":371973,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5135/sir20195135.pdf","text":"Report","size":"10.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5135"},{"id":370724,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370723,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370721,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A682GW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southeastern United States, 2012 base year"},{"id":370722,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-southeast-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370726,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":371031,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5135/coverthb3.jpg"}],"otherGeospatial":"Southeastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2890625,\n 37.19533058280065\n ],\n [\n -81.34277343749999,\n 37.47485808497102\n ],\n [\n -83.9794921875,\n 34.95799531086792\n ],\n [\n -88.8134765625,\n 33.8339199536547\n ],\n [\n -90.1318359375,\n 33.43144133557529\n ],\n [\n -90.2197265625,\n 30.44867367928756\n ],\n [\n -88.154296875,\n 30.29701788337205\n ],\n [\n -85.69335937499999,\n 29.878755346037977\n ],\n [\n -84.72656249999999,\n 29.49698759653577\n ],\n [\n -84.111328125,\n 29.84064389983441\n ],\n [\n -82.96875,\n 27.955591004642553\n ],\n [\n -82.5732421875,\n 26.54922257769204\n ],\n [\n -80.9033203125,\n 25.20494115356912\n ],\n [\n -80.0244140625,\n 25.3241665257384\n ],\n [\n -79.8486328125,\n 27.527758206861886\n ],\n [\n -80.85937499999999,\n 30.031055426540206\n ],\n [\n -80.771484375,\n 31.615965936476076\n ],\n [\n -78.57421875,\n 33.50475906922609\n ],\n [\n -77.16796875,\n 33.97980872872457\n ],\n [\n -76.11328125,\n 34.95799531086792\n ],\n [\n -75.41015624999999,\n 35.96022296929667\n ],\n [\n -75.8056640625,\n 36.80928470205937\n ],\n [\n -76.2890625,\n 37.19533058280065\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
In this report, SPAtially Referenced Regression On Watershed attributes (SPARROW) models developed to describe long-term (2000–14) mean-annual streamflow, total nitrogen (TN), total phosphorus (TP), and suspended-sediment (SS) transport in streams of the Midwestern part of the United States (the Mississippi River, Great Lakes, and Red River of the North Basins) are described. The nutrient and suspended-sediment models have a base year of 2012, which means they were developed based on source inputs and management practices similar to those existing during or near 2012 and average hydrological conditions detrended to 2012 (2000–14), whereas the streamflow model has base years of 2000–14, which means it was developed based on the average input precipitation minus actual evapotranspiration from 2000 to 2014. In developing the models, several updates and improvements were made to the data inputs and statistical approaches used to calibrate/develop the models from those used in the previous 2002 SPARROW models. The 2012 SPARROW models were constructed using a higher resolution stream network, which resulted in a mean catchment size of 2.7 square kilometers compared to 480 square kilometers in the 2002 models; more detailed and updated wastewater treatment plant contribution estimates; inputs from background phosphorus sources that were not included in the 2002 model; and more accurate loads for calibration that were computed using a modified Beale ratio-estimator technique whenever no trend in load was determined. Statistical approaches were added to compensate for the unequal effect of each monitoring site during the calibration process by adjusting for the fraction of the basin included in other upstream monitored sites (nested share) and thinning the calibration sites if a negative statistical correlation between nearby sites was determined.
Results from 2012 SPARROW models describe how much of each water, TN, TP, and SS source was delivered to the stream network, and the major landscape factors that affected their delivery. Atmospheric deposition and natural (background) sources of TN and TP, respectively, were the dominant sources in anthropogenically unaffected areas (especially in the Rocky Mountains and north-central areas of the Midwest), whereas fertilizers, manure, and fixation were dominant sources in agricultural areas, especially in the Corn Belt and near the Mississippi River. Urban sources of TN and TP were typically localized, but they were still important for some large areas, especially the Lake Erie Basin. All of the land-to-water delivery variables in the nutrient and sediment SPARROW models, such as runoff, soil erodibility, basin slope, and the amount of tile drains, are commonly included in process-driven models. In the SPARROW TN and TP models, best management practices (BMPs) reduced the delivery of these nutrients to streams.
Long-term mean-annual flows and nutrient and sediment loads were simulated in streams throughout the Midwest. The simulated flows from the SPARROW flow model were used in the SPARROW TN, TP, and SS models to help describe nutrient and sediment transport from the watershed and through the stream network. Outputs from the TN, TP, and SS models describe loads and yields of these constituents throughout the Midwest, and from major drainage basins throughout the Midwest. Highest TN, TP, and SS yields and delivered yields were from the Lake Erie, Ohio River, Upper Mississippi River, and Lower Mississippi River Basins, whereas lowest yields were spread over most other areas. Losses during downstream delivery resulted in part of the TN, TP, and SS that reach the stream network not reaching the downstream receiving bodies: 14, 15, and 28 percent of the TN, TP, and SS, respectively, are lost during delivery to the Great Lakes and 19, 23, and 52 percent of the TN, TP, and SS, respectively, are lost during delivery to the Gulf of Mexico. The largest losses of nutrients and sediments during transport were in the Missouri and Arkansas River Basins.
Information from these SPARROW models can help guide nutrient and sediment reduction strategies throughout the Midwest. Model results provide information on what may be the most appropriate general type of actions to reduce total loading by describing the relative importance of each source, and where to most efficiently place the efforts to reduce loading by describing the distribution of nutrient and sediment loading. By implementing management efforts addressing the major sources of the loads in areas contributing the highest loads, it may be possible to reduce nutrient loading throughout the Mississippi River Basin and thus reduce the size of the hypoxic zone in the Gulf of Mexico; reduce nutrient loading into lakes, and thus reduce the occurrence of harmful algal blooms; and reduce sediment losses, and thus improve the benthic habitat in streams and rivers throughout the Midwest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195114","collaboration":"National Water Quality Program","usgsCitation":"Robertson, D.M., and Saad, D.A., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Midwestern United States: U.S. Geological Survey Scientific Investigations Report 2019–5114, 74 p. including 5 appendixes, https://doi.org/10.3133/sir20195114.","productDescription":"Report: ix, 74 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103244","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":370714,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"},{"id":370711,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370712,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":371971,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5114/sir20195114.pdf","text":"Report","size":"43.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5114"},{"id":370371,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-midwest-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370713,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":370369,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93QMXC9","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Midwestern United States, 2012 base year"},{"id":370914,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5114/coverthb3.jpg"}],"otherGeospatial":"Midwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125,\n 44.213709909702054\n ],\n [\n -79.27734374999999,\n 43.389081939117496\n ],\n [\n -79.541015625,\n 42.61779143282346\n ],\n [\n -82.529296875,\n 41.44272637767212\n ],\n [\n -82.177734375,\n 43.068887774169625\n ],\n [\n -82.705078125,\n 45.02695045318546\n ],\n [\n -84.287109375,\n 46.6795944656402\n ],\n [\n -90,\n 47.931066347509784\n ],\n [\n -94.5703125,\n 49.210420445650286\n ],\n [\n -95.361328125,\n 49.26780455063753\n ],\n [\n -95.44921875,\n 48.922499263758255\n ],\n [\n -114.2578125,\n 49.095452162534826\n ],\n [\n -113.90625,\n 46.73986059969267\n ],\n [\n -112.236328125,\n 45.336701909968134\n ],\n [\n -110.12695312499999,\n 42.48830197960227\n ],\n [\n -106.69921875,\n 38.06539235133249\n ],\n [\n -101.42578124999999,\n 32.99023555965106\n ],\n [\n -97.20703125,\n 28.536274512989916\n ],\n [\n -92.900390625,\n 29.305561325527698\n ],\n [\n -89.912109375,\n 33.063924198120645\n ],\n [\n -79.541015625,\n 39.774769485295465\n ],\n [\n -76.11328125,\n 44.213709909702054\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Given the predicted imbalance between water supply and demand in the Southwest region of the United States, and the widespread problems with excessive nutrients and suspended sediment, there is a growing need to quantify current streamflow and water quality conditions throughout the region. Furthermore, current monitoring stations exist at a limited number of locations, and many streams lack streamflow and water quality information. SPAtially Referenced Regression On Watershed attributes (SPARROW) models were developed for hydrologic conditions representative of 2012 in order to understand how climate, land use, and other landscape characteristics control the yields of water, total nitrogen, total phosphorus, and suspended sediment across the Southwest region. The calibration data (mean annual streamflow and loads) for each of the four SPARROW models were based on continuous streamflow and discrete water-quality observations from throughout the region. Explanatory variables for the models consisted of regional datasets representing a range of potential sources of streamflow, nitrogen, phosphorous, and sediment, and processes that control the transport from land to water and attenuate loads within streams and waterbodies. Calibration and explanatory data were referenced to a surface water drainage network that allowed for routing and transport of water and loads through the region. The model results showed that wastewater discharge is the largest contributor to total nitrogen and total phosphorus yield from the Southwest region and forest land is the largest contributor to suspended-sediment yield, but that other sources such as atmospheric nitrogen deposition, agricultural runoff, and runoff from developed land are locally important across the region. The results from this study could complement research and inform water-quality management activities in the Southwest region. Examples might include identifying potentially impaired waterbodies and guiding remediation efforts where impairment has been documented, explaining the spatial patterns in harmful algal blooms, and providing estimates of sediment and nutrient loadings where such data are scarce or non-existent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195106","collaboration":"National Water Quality Program","usgsCitation":"Wise, D.R., Anning, D.W., and Miller, O.L., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment transport in streams of the southwestern United States (ver. 1.1, June 2020): U.S. Geological Survey Scientific Investigations Report 2019-5106, 66 p., https://doi.org/10.3133/sir20195106.","productDescription":"Report: viii, 66 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105772","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":437233,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94EKLPP","text":"USGS data release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southwestern United States, 2012 Base Year (ver. 2.0, October 2020)"},{"id":370350,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5106/coverthb.jpg"},{"id":371969,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5106/sir20195106.pdf","text":"Report","size":"21.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5106"},{"id":370352,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GFLBG8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southwestern United States, 2012 base year"},{"id":370377,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-southwest-2012","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370703,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370704,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":370705,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":376099,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5106/VersionHist.txt"},{"id":370706,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"}],"country":"United States","otherGeospatial":"Southwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.927734375,\n 32.39851580247402\n ],\n [\n -114.873046875,\n 32.76880048488168\n ],\n [\n -111.09374999999999,\n 31.203404950917395\n ],\n [\n -108.28125,\n 31.052933985705163\n ],\n [\n -108.19335937499999,\n 32.02670629333614\n ],\n [\n -105.8203125,\n 30.977609093348686\n ],\n [\n -103.447265625,\n 28.76765910569123\n ],\n [\n -102.83203125,\n 29.6880527498568\n ],\n [\n -101.689453125,\n 29.76437737516313\n ],\n [\n -100.1953125,\n 27.916766641249065\n ],\n [\n -98.349609375,\n 26.115985925333536\n ],\n [\n -97.20703125,\n 26.509904531413927\n ],\n [\n -96.767578125,\n 28.22697003891834\n ],\n [\n -93.515625,\n 29.53522956294847\n ],\n [\n -99.49218749999999,\n 33.87041555094183\n ],\n [\n -103.623046875,\n 38.8225909761771\n ],\n [\n -104.94140625,\n 40.245991504199026\n ],\n [\n -111.97265625,\n 42.94033923363181\n ],\n [\n -113.90625,\n 41.376808565702355\n ],\n [\n -116.806640625,\n 41.77131167976407\n ],\n [\n -118.740234375,\n 44.213709909702054\n ],\n [\n -120.498046875,\n 44.59046718130883\n ],\n [\n -120.84960937499999,\n 41.44272637767212\n ],\n [\n -118.65234374999999,\n 36.10237644873644\n ],\n [\n -115.927734375,\n 32.39851580247402\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: January 7, 2020; Version 1.1: June 2, 2020","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Although spatial information describing the supply and quality of surface water is critical for managing water resources for human uses and for ecological health, monitoring is expensive and cannot typically be done over large scales or in all streams or waterbodies. To address the need for such data, the U.S. Geological Survey developed SPAtially Referenced Regression On Watershed attributes (SPARROW) for the Pacific region of the U.S. for streamflow and three water-quality constituents–total nitrogen, total phosphorus, and suspended sediment, based on a decadal time frame centered on the year 2012. The domain for these models included the Columbia River basin, the Puget Sound, the coastal drainages of Washington, Oregon, and California, and the Central Valley of California. Landscape runoff (represented by the difference between precipitation and evapotranspiration) was the largest source of streamflow, wastewater discharge, and atmospheric deposition were the largest contributors to total nitrogen yield from the Pacific region, wastewater discharge was the largest contributor to total phosphorus yield, and forest land was the largest contributor to suspended-sediment yield. Watersheds with relatively high water yields also generally had relatively high yields of total nitrogen, total phosphorous, and suspended sediment–except where there were large contributions from developed land and wastewater discharge.
The data used in this study, including many that improved upon existing national data or were compiled specifically for the Pacific region, characterized the complex hydrologic and water-quality conditions in the region more completely than previous models. By using these new datasets, this investigation was able to account for the complex network of water diversions and transfers, quantify the contribution of nutrients from different sources of livestock manure, discern a signal from unpaved logging roads in the suspended-sediment yields from forested coastal watersheds, show how recent wildfire disturbance influences phosphorus and sediment delivery to streams, and how sediment delivery to streams is also sensitive to the intensity of cattle grazing. The results from this study could complement research and inform water-quality management activities in the Pacific region. Examples might include identifying potentially impaired waterbodies and guiding remediation efforts where impairment has been documented, explaining the spatial patterns in harmful algal blooms, and providing estimates of sediment and nutrient loadings to Pacific coast estuaries where such data are scarce or non-existent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195112","collaboration":"National Water Quality Program","usgsCitation":"Wise, D.R., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Pacific region of the United States (ver. 1.1, June 2020): U.S. Geological Survey Scientific Investigations Report 2019-5112, 64 p., https://doi.org/10.3133/sir20195112.","productDescription":"Report: x, 64 p.; Data Release; Application Site; Companion Files; Version History; Read Me","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103780","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":437240,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V1RY3N","text":"USGS data release","linkHelpText":"Application of nutrients generated by non-cattle livestock to farmland within the Pacific drainages of the United States, 2012"},{"id":437239,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92EZQO3","text":"USGS data release","linkHelpText":"Distribution of grazing cattle within the Pacific drainages of the United State, 2012"},{"id":437238,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SCZMJB","text":"USGS data release","linkHelpText":"Application of manure nutrients generated at cattle animal feeding operations to farmland within the Pacific drainages of the United State, 2012"},{"id":437237,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCPKPC","text":"USGS data release","linkHelpText":"Application of manure nutrients generated by grazing cattle to grazing land within the Pacific drainages of the United States, 2012"},{"id":437236,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MYHLJ6","text":"USGS data release","linkHelpText":"County-level livestock data for the Pacific drainages of the United States, 2012"},{"id":437235,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P979BBCQ","text":"USGS data release","linkHelpText":"Population with On-Site Wastewater Treatment within the Pacific Drainages of the United States, 2010"},{"id":437234,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98PDDT1","text":"USGS data release","linkHelpText":"Potential Grazing Land Within the Pacific Drainages of the Western United States, 2011"},{"id":375959,"rank":10,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5112/VersionHist.txt","description":"Version History"},{"id":370710,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"},{"id":371970,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5112/sir20195112.pdf","text":"Report","size":"31.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5112"},{"id":373211,"rank":9,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sir/2019/5112/CorrectionNotes.txt","text":"Correction notes","size":"918 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2019-5112"},{"id":370363,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AXLOSM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Pacific Region of the United States, 2012 base year"},{"id":370708,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":370709,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":371018,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5112/coverthb2.jpg"},{"id":370387,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-pacific-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370707,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"}],"country":"United States","otherGeospatial":"Pacific Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.42187500000001,\n 33.7243396617476\n ],\n [\n -120.58593749999999,\n 38.54816542304656\n ],\n [\n -120.58593749999999,\n 42.16340342422401\n ],\n [\n -118.828125,\n 42.16340342422401\n ],\n [\n -117.24609374999999,\n 43.58039085560784\n ],\n [\n -113.5546875,\n 44.84029065139799\n ],\n [\n -115.83984375,\n 48.3416461723746\n ],\n [\n -119.53125,\n 49.38237278700955\n ],\n [\n -124.27734374999999,\n 48.922499263758255\n ],\n [\n -124.98046874999999,\n 47.39834920035926\n ],\n [\n -124.8046875,\n 43.70759350405294\n ],\n [\n -124.45312499999999,\n 38.41055825094609\n ],\n [\n -120.76171875,\n 33.87041555094183\n ],\n [\n -117.42187500000001,\n 33.7243396617476\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: January 2020; Version 1.1: June 2020","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP−III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the station skew for a given streamgage and a regional skew. This report generates an estimate of regional skew for a study area encompassing most of the Great Lakes Basin (hydrologic unit 04) and part of the Ohio River Basin (hydrologic unit 05). A total of 551 candidate streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length greater than 36 years, 368 streamgages were selected for use in the study. Flood frequencies for candidate streamgages were analyzed by employing the Expected Moments Algorithm, which extends the method of moments so that it can accommodate interval, censored, and historic/paleo flow data, as well as the Multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Twelve basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 5 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model having a regional skew coefficient of 0.086 and an average variance of prediction (AVPnew) (which corresponds to the mean square error [MSE]) of 0.13 at a new streamgage was selected. The AVPnew corresponds to an effective record length of 54 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195105","usgsCitation":"Veilleux, A.G., and Wagner, D.M., 2019, Methods for estimating regional skewness of annual peak flows in parts of the Great Lakes and Ohio River Basins, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2019–5105, 26 p., https://doi.org/10.3133/sir20195105.","productDescription":"Report: vi, 25 p.; 5 Figures; Table; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101994","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":371689,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_table1.xlsx","text":"Table 1","size":"99.5 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Streamgages in parts of the Great Lakes and Ohio River Basins considered for use in regional skew analysis"},{"id":371684,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01a.pdf","text":"Figure 1A","size":"5.25 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing 4-digit hydrologic units"},{"id":371685,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01b.pdf","text":"Figure 1B","size":"2.41 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing locations of streamgages used in skew analysis"},{"id":371682,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N7UAFJ","text":"USGS data release","linkHelpText":"Annual peak-flow data, PeakFQ specification files and PeakFQ output files for 368 selected streamflow gaging stations operated by the U.S. Geological Survey in the Great Lakes and Ohio River basins that were used to estimate regional skewness of annual peak flows"},{"id":371681,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5105"},{"id":371680,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5105/coverthb.jpg"},{"id":399546,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109629.htm"},{"id":371688,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig05.pdf","text":"Figure 5","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing residuals from constant model of skew for 368 streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"},{"id":371687,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig03.pdf","text":"Figure 3","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing unbiased station skew of streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"},{"id":371686,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig02.pdf","text":"Figure 2","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing the pseudo record lengths of streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"}],"country":"United States, Canada","otherGeospatial":"Great Lakes and Ohio River Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3740234375,\n 48.99463598353405\n ],\n [\n -89.89013671875,\n 48.22467264956519\n ],\n [\n -91.62597656249999,\n 47.70976154266637\n ],\n [\n -92.900390625,\n 46.92025531537451\n ],\n [\n -92.900390625,\n 46.14939437647686\n ],\n [\n -92.0654296875,\n 45.49094569262732\n ],\n [\n -91.2744140625,\n 45.321254361171476\n ],\n [\n -90.24169921875,\n 45.058001435398275\n ],\n [\n -89.75830078125,\n 43.929549935614595\n ],\n [\n -88.65966796875,\n 42.58544425738491\n ],\n [\n -88.00048828124999,\n 42.00032514831621\n ],\n [\n -87.5390625,\n 41.393294288784865\n ],\n [\n -86.11083984375,\n 41.672911819602085\n ],\n [\n -86.484375,\n 41.1455697310095\n ],\n [\n -87.7587890625,\n 40.094882122321145\n ],\n [\n -88.72558593749999,\n 39.436192999314095\n ],\n [\n -89.07714843749999,\n 38.51378825951165\n ],\n [\n -89.47265625,\n 37.125286284966805\n ],\n [\n -89.07714843749999,\n 36.56260003738545\n ],\n [\n -88.2861328125,\n 36.56260003738545\n ],\n [\n -87.802734375,\n 35.782170703266075\n ],\n [\n -86.748046875,\n 35.10193405724606\n ],\n [\n -85.25390625,\n 35.10193405724606\n ],\n [\n -84.3310546875,\n 35.496456056584165\n ],\n [\n -83.49609375,\n 36.66841891894786\n ],\n [\n -81.9140625,\n 37.33522435930639\n ],\n [\n -81.34277343749999,\n 37.16031654673677\n ],\n [\n -81.38671875,\n 36.24427318493909\n ],\n [\n -80.8154296875,\n 36.10237644873644\n ],\n [\n -79.89257812499999,\n 36.73888412439431\n ],\n [\n -79.62890625,\n 37.26530995561875\n ],\n [\n -79.453125,\n 38.03078569382294\n ],\n [\n -78.44238281249999,\n 38.51378825951165\n ],\n [\n -77.6953125,\n 40.3130432088809\n ],\n [\n -76.904296875,\n 42.09822241118974\n ],\n [\n -75.673828125,\n 42.87596410238256\n ],\n [\n -74.4873046875,\n 43.61221676817573\n ],\n [\n -73.5205078125,\n 44.465151013519616\n ],\n [\n -72.59765625,\n 43.96119063892024\n ],\n [\n -71.630859375,\n 44.49650533109348\n ],\n [\n -70.6640625,\n 44.809121700077355\n ],\n [\n -69.873046875,\n 46.042735653846506\n ],\n [\n -69.0380859375,\n 47.30903424774781\n ],\n [\n -69.7412109375,\n 48.1367666796927\n ],\n [\n -71.1474609375,\n 47.54687159892238\n ],\n [\n -71.806640625,\n 46.76996843356982\n ],\n [\n -71.7626953125,\n 45.73685954736049\n ],\n [\n -72.7294921875,\n 45.36758436884978\n ],\n [\n -74.00390625,\n 46.10370875598026\n ],\n [\n -74.4873046875,\n 45.920587344733654\n ],\n [\n -74.6630859375,\n 45.398449976304086\n ],\n [\n -75.146484375,\n 45.02695045318546\n ],\n [\n -76.5087890625,\n 44.15068115978094\n ],\n [\n -77.080078125,\n 44.18220395771566\n ],\n [\n -78.22265625,\n 44.11914151643737\n ],\n [\n -79.62890625,\n 43.739352079154706\n ],\n [\n -79.716796875,\n 43.48481212891603\n ],\n [\n -81.2548828125,\n 42.71473218539458\n ],\n [\n -82.6171875,\n 42.19596877629178\n ],\n [\n -82.353515625,\n 42.87596410238256\n ],\n [\n -81.474609375,\n 43.89789239125797\n ],\n [\n -81.2109375,\n 45.058001435398275\n ],\n [\n -80.0244140625,\n 44.276671273775186\n ],\n [\n -79.5849609375,\n 44.87144275016589\n ],\n [\n -80.5517578125,\n 46.01222384063236\n ],\n [\n -81.9140625,\n 46.13417004624326\n ],\n [\n -83.3203125,\n 46.255846818480315\n ],\n [\n -84.375,\n 46.86019101567027\n ],\n [\n -84.375,\n 47.54687159892238\n ],\n [\n -84.7705078125,\n 48.16608541901253\n ],\n [\n -85.78125,\n 47.96050238891509\n ],\n [\n -86.0009765625,\n 48.69096039092549\n ],\n [\n -86.66015624999999,\n 49.095452162534826\n ],\n [\n -88.3740234375,\n 48.99463598353405\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Integrated Modeling and Prediction Division
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The U.S. Geological Survey National Crustal Model (NCM) is being developed to assist with earthquake hazard and risk assessment by supporting estimates of ground shaking in response to an earthquake. The period-dependent intensity and duration of shaking depend upon the three-dimensional seismic velocity, seismic attenuation, and density distribution of a region, which in turn is governed to a large degree by geology and how that geology behaves under varying temperatures and pressures.
A three-dimensional temperature model is presented here to support the estimation of physical parameters within the U.S. Geological Survey NCM. The crustal model is defined by a geological framework consisting of various lithologies with distinct mineral compositions. A temperature model is needed to calculate mineral density and bulk and shear modulus as a function of position within the crust. These properties control seismic velocity and impedance, which are needed to accurately estimate earthquake travel times and seismic amplitudes in earthquake hazard analyses. The temperature model is constrained by observations of surface temperature, temperature gradient, and conductivity, inferred Moho temperature and depth, and assumed conductivity at the base of the crust. The continental plate is assumed to have heat production that decreases exponentially with depth and thermal conductivity that exponentially changes from a surface value to 3.6 watts per meter-Kelvin at the Moho. The oceanic plate cools as a half-space with a geotherm dependent on plate age. Under these conditions, and the application of observed surface heat production, predicted Moho temperatures match Moho temperatures inferred from seismic P-wave velocities, on average. As has been noted in previous studies, high crustal temperatures are found in the western United States, particularly beneath areas of recent volcanism. In the central and eastern United States, elevated temperatures are found from southeast Texas, into the Mississippi Embayment, and up through Wisconsin. A USGS ScienceBase data release that supports this report is available and consists of grids covering the NCM across the conterminous United States, for example, surface temperature and temperature gradient, that are needed to produce temperature profiles.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191121","usgsCitation":"Boyd, O.S., 2020, Temperature model in support of the U.S. Geological Survey National Crustal Model for seismic hazard studies: U.S. Geological Survey Open-File Report 2019–1121, 15 p., https://doi.org/10.3133/ofr20191121.","productDescription":"Report: iv, 15 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-109788","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":437241,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SL2PVR","text":"USGS data release","linkHelpText":"TherMod"},{"id":399419,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109626.htm"},{"id":371516,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P935DT1G","text":"USGS data release","linkHelpText":"Grids in support of the U.S. Geological Survey Thermal Model for Seismic Hazard Studies"},{"id":371515,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1121/ofr20191121.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1121"},{"id":371514,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1121/coverthb.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}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
To address the lack of information on the spatial and temporal variability of recharge to groundwater systems in Maine, a study was initiated in cooperation with the Maine Geological Survey to use the U.S. Geological Survey Soil-Water-Balance model to evaluate annual average potential recharge across the State over a 25-year period from 1991 to 2015. The Maine Soil-Water-Balance model was calibrated using annual observations of recharge, runoff, and evapotranspiration for 32 calibration watersheds in the State during 2001–12 (902 total observations). Observations of recharge, runoff, and evapotranspiration were developed for each watershed to reduce the possibility of nonunique combinations of model parameters during the calibration. The Maine Soil-Water-Balance model was run using an optional evapotranspiration calculation method that provides more control for calibration than the standard method. The model was calibrated using the Parameter ESTimation software suite.
The overall mean model error (average of all annual residuals for recharge, runoff, and precipitation) was 0.39 inch. The mean of the absolute value of the residuals, or the mean absolute error, was 2.32 inches. The root mean squared error for the calibrated model overall was 3.14 inches. Statistical tests indicated that the model residuals are normally distributed. To determine the potential uncertainty in the median annual potential recharge that results from uncertainty in the parameters as they relate to information contained in the observations, 300 alternate model realizations were run, and the standard deviation of the median potential recharge value at every pixel was calculated.
Simulated 25-year median potential recharge across the State is widely variable; this variability closely follows patterns of precipitation, with additional variability contributed by the patchwork nature of the combinations of land-use class and hydrologic soil group inputs, and distribution of available water capacity in the soil across the State. Overall, the 25-year median annual potential recharge across the State is 7.5 inches, ranging from a low of about 5 inches to over 30 inches. The statewide range in the 25-year minimum values is from just over 2 inches to just over 20 inches. The statewide range in the 25-year maximum potential recharge is between 15 and 48 inches per year.
The model areas with the highest simulated median potential recharge include areas underlain by type A soils (sandy and well drained), particularly those that also have land uses with low or little vegetation (blueberry barrens, developed, open space, scrub/shrub, and cropland, for example). The potential recharge values for these areas are similar to previously published values for comparable soil types.
The 25-year average potential recharge grids were compared to recharge evaluated through groundwater-flow models or other methods in four hydrogeologic settings at six study areas in the State. A key factor in the ability of the Soil-Water-Balance model to reproduce the earlier study results was whether the available water-capacity data were an appropriate match for the hydrologic soil groups. The Maine Soil-Water-Balance model does a good job in representing an accurate potential recharge under circumstances where the surficial mapped soils extend below the surface to the water-table aquifer and where the available water-capacity data are in an appropriate range for the hydrologic soil group. One hydrogeologic setting that was challenging for the model was where a silt and clay layer was below a shallow soil unit that did not have available water-capacity data that were appropriate for the hydrologic soil group. In these cases, typically the available water-capacity data were very low, not accounting for the impedance of water flow provided by the underlying soil. The model also does not simulate well areas where bedrock surfaces are above the water table but below the plant rooting zone.
The data products accompanying this report are intended to be used to provide first-cut estimates of recharge for geographic areas no smaller than the smallest watersheds used in the calibration of the model—or about 1.5 square miles. It is recommended that the grids are used to calculate an area-wide average potential recharge for any given area of study, and an uncertainty around the mean should be calculated from the standard deviation grid at the same time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195125","collaboration":"Prepared in cooperation with the Maine Geological Survey","usgsCitation":"Nielsen, M.G., and Westenbroek, S.M., 2019, Groundwater recharge estimates for Maine using a Soil-Water-Balance model—25-year average, range, and uncertainty, 1991 to 2015: U.S. Geological Survey Scientific Investigations Report 2019–5125, 58 p., https://doi.org/10.3133/sir20195125.","productDescription":"Report: vii, 56 p.; Tables; 2 Data Releases","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106360","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380621,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2019/5125/images/"},{"id":374942,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2019/5125/sir20195125.XML"},{"id":371640,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5125/sir20195125.pdf","text":"Report","size":"5.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5125"},{"id":371639,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5125/coverthb3.jpg"},{"id":370509,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2019/5125/sir20195125_appendixtables.zip","text":"Tables","size":"48.5 KB","linkFileType":{"id":6,"text":"zip"}},{"id":370508,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9052ULY","text":"USGS data release","linkHelpText":"Simulated 25-year median potential recharge datasets for Maine, 1991–2015"},{"id":370507,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GRP7DH","text":"USGS data release","linkHelpText":"Soil-Water-Balance (SWB) model archive used to simulate potential annual recharge in Maine, 1991–2015"},{"id":399606,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109568.htm"}],"country":"United States","state":"Maine","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-70.152589,43.746794],[-70.158456,43.751616],[-70.147646,43.758585],[-70.145911,43.772119],[-70.128271,43.774009],[-70.14089,43.753204],[-70.152589,43.746794]]],[[[-70.135957,43.753219],[-70.129721,43.76408],[-70.117688,43.765693],[-70.135957,43.753219]]],[[[-70.171245,43.663498],[-70.205934,43.633633],[-70.211062,43.641842],[-70.200116,43.662978],[-70.188047,43.673762],[-70.171245,43.663498]]],[[[-70.186213,43.682655],[-70.210825,43.661695],[-70.213948,43.666161],[-70.201893,43.685483],[-70.191041,43.689071],[-70.186213,43.682655]]],[[[-70.163884,43.692404],[-70.146115,43.701635],[-70.135563,43.700658],[-70.154503,43.680933],[-70.168227,43.675136],[-70.173571,43.683734],[-70.163884,43.692404]]],[[[-70.087621,43.699913],[-70.093704,43.6918],[-70.099594,43.695366],[-70.115908,43.682978],[-70.118174,43.686375],[-70.093113,43.710524],[-70.097184,43.700929],[-70.087621,43.699913]]],[[[-70.119671,43.748621],[-70.097318,43.757292],[-70.094986,43.753211],[-70.107812,43.734555],[-70.108978,43.722312],[-70.129383,43.70832],[-70.138128,43.718231],[-70.138711,43.727559],[-70.119671,43.748621]]],[[[-68.499465,44.12419],[-68.491521,44.109833],[-68.502942,44.099722],[-68.51706,44.10341],[-68.518703,44.113222],[-68.511266,44.125082],[-68.499465,44.12419]]],[[[-68.358388,44.125082],[-68.346724,44.127749],[-68.331032,44.10758],[-68.338012,44.101473],[-68.365176,44.101464],[-68.375382,44.11646],[-68.358388,44.125082]]],[[[-68.453236,44.189998],[-68.416434,44.187047],[-68.384903,44.154955],[-68.396634,44.14069],[-68.438518,44.11618],[-68.448646,44.125581],[-68.447505,44.133493],[-68.456813,44.145268],[-68.496639,44.146855],[-68.502096,44.152388],[-68.500817,44.160026],[-68.474365,44.181875],[-68.453236,44.189998]]],[[[-68.680773,44.279242],[-68.623554,44.255622],[-68.605906,44.230772],[-68.612749,44.207722],[-68.624994,44.197637],[-68.618872,44.18107],[-68.643002,44.15766],[-68.670014,44.151537],[-68.675056,44.137131],[-68.681899,44.138212],[-68.692343,44.153698],[-68.713232,44.160541],[-68.720435,44.169185],[-68.714313,44.20376],[-68.722956,44.219607],[-68.718635,44.228611],[-68.700627,44.234013],[-68.680458,44.262105],[-68.680773,44.279242]]],[[[-68.355279,44.199096],[-68.333227,44.207308],[-68.314789,44.197157],[-68.321178,44.199032],[-68.332639,44.192131],[-68.339029,44.171839],[-68.347416,44.169459],[-68.378872,44.184222],[-68.364469,44.197534],[-68.355279,44.199096]]],[[[-68.472831,44.219767],[-68.453843,44.201683],[-68.459182,44.197681],[-68.48452,44.202886],[-68.482726,44.227058],[-68.470323,44.22832],[-68.472831,44.219767]]],[[[-68.792139,44.237819],[-68.769833,44.222787],[-68.769047,44.213351],[-68.780055,44.203129],[-68.829593,44.21689],[-68.839422,44.236547],[-68.827627,44.242838],[-68.792139,44.237819]]],[[[-68.23638,44.266254],[-68.214641,44.263156],[-68.211329,44.257074],[-68.24031,44.251622],[-68.240806,44.239723],[-68.248913,44.235443],[-68.274427,44.237099],[-68.274719,44.258675],[-68.246598,44.257836],[-68.23638,44.266254]]],[[[-68.498637,44.369686],[-68.478785,44.319563],[-68.489641,44.313705],[-68.530394,44.333583],[-68.518573,44.381022],[-68.501364,44.382281],[-68.498637,44.369686]]],[[[-68.618212,44.012367],[-68.635315,44.018886],[-68.657031,44.003823],[-68.659874,44.022758],[-68.650767,44.039908],[-68.661594,44.075837],[-68.627893,44.088128],[-68.6181,44.096706],[-68.609722,44.094674],[-68.584074,44.070578],[-68.590792,44.058662],[-68.601099,44.058362],[-68.610703,44.013422],[-68.618212,44.012367]]],[[[-68.785601,44.053503],[-68.818441,44.032046],[-68.874139,44.025359],[-68.889717,44.032516],[-68.899997,44.06696],[-68.913406,44.08519],[-68.908984,44.110001],[-68.944597,44.11284],[-68.917286,44.148239],[-68.847249,44.183017],[-68.825067,44.186338],[-68.819156,44.180462],[-68.82284,44.173693],[-68.818423,44.160978],[-68.782375,44.14531],[-68.792065,44.136759],[-68.818039,44.136852],[-68.820515,44.130198],[-68.815562,44.115836],[-68.806832,44.116339],[-68.790525,44.09292],[-68.772639,44.078439],[-68.77029,44.069566],[-68.785601,44.053503]]],[[[-67.619761,44.519754],[-67.582113,44.513459],[-67.590627,44.49415],[-67.562651,44.472104],[-67.571774,44.453403],[-67.588346,44.449754],[-67.604919,44.502056],[-67.619211,44.506009],[-67.619761,44.519754]]],[[[-68.942826,44.281073],[-68.919301,44.309872],[-68.919325,44.335392],[-68.90353,44.378613],[-68.87894,44.386584],[-68.868444,44.38144],[-68.860649,44.364425],[-68.87169,44.344662],[-68.89285,44.334653],[-68.896587,44.321986],[-68.88746,44.303094],[-68.904255,44.279889],[-68.916872,44.242866],[-68.95189,44.218719],[-68.94709,44.226792],[-68.955332,44.243873],[-68.965896,44.249754],[-68.965264,44.259332],[-68.942826,44.281073]]],[[[-70.353392,43.535405],[-70.379123,43.507202],[-70.385615,43.487031],[-70.380233,43.46423],[-70.349684,43.442032],[-70.370514,43.434133],[-70.384949,43.418839],[-70.39089,43.402607],[-70.421282,43.395777],[-70.427672,43.389254],[-70.424986,43.375928],[-70.460717,43.34325],[-70.517695,43.344037],[-70.553854,43.321886],[-70.593907,43.249295],[-70.576692,43.217651],[-70.618973,43.163625],[-70.638355,43.114182],[-70.655322,43.098008],[-70.665958,43.076234],[-70.703818,43.059825],[-70.708896,43.074989],[-70.737897,43.073488],[-70.756397,43.079988],[-70.766398,43.092688],[-70.779098,43.095887],[-70.8268,43.127086],[-70.8338,43.146886],[-70.823501,43.174585],[-70.828301,43.186685],[-70.819146,43.195157],[-70.811852,43.228306],[-70.817773,43.237408],[-70.837274,43.242321],[-70.843302,43.254321],[-70.858207,43.256286],[-70.861384,43.263143],[-70.881704,43.272483],[-70.886504,43.282783],[-70.906005,43.291682],[-70.900386,43.301358],[-70.91246,43.308289],[-70.912004,43.319821],[-70.93711,43.337367],[-70.956528,43.334691],[-70.967229,43.343777],[-70.985965,43.380023],[-70.98739,43.393457],[-70.982565,43.39778],[-70.987249,43.411863],[-70.96115,43.438321],[-70.9669,43.450458],[-70.961428,43.469696],[-70.974245,43.47742],[-70.967968,43.480783],[-70.954755,43.509802],[-70.954066,43.52261],[-70.963281,43.538929],[-70.950838,43.551026],[-70.972716,43.570255],[-70.989037,43.792154],[-71.031039,44.655455],[-71.084334,45.305293],[-71.059265,45.313753],[-71.030565,45.312652],[-71.00905,45.319022],[-71.002563,45.327819],[-71.011144,45.334679],[-71.01081,45.34725],[-70.985595,45.332188],[-70.950824,45.33453],[-70.939188,45.320177],[-70.917904,45.311924],[-70.912111,45.296197],[-70.9217,45.279445],[-70.898565,45.258502],[-70.898482,45.244088],[-70.885029,45.234873],[-70.857042,45.22916],[-70.83877,45.237555],[-70.848319,45.244707],[-70.848554,45.263325],[-70.839042,45.269132],[-70.829661,45.290369],[-70.812338,45.302006],[-70.808613,45.311606],[-70.808322,45.325824],[-70.816585,45.330554],[-70.819828,45.340109],[-70.81445,45.356544],[-70.803848,45.364247],[-70.806244,45.376558],[-70.826033,45.398408],[-70.795009,45.428145],[-70.755567,45.428361],[-70.744077,45.421091],[-70.743775,45.411925],[-70.729972,45.399359],[-70.712286,45.390611],[-70.677995,45.394362],[-70.66116,45.386039],[-70.660775,45.378176],[-70.651175,45.377123],[-70.634661,45.383608],[-70.631354,45.41634],[-70.635498,45.427817],[-70.649739,45.442771],[-70.674903,45.452399],[-70.691762,45.471233],[-70.717047,45.487732],[-70.721611,45.515058],[-70.687605,45.549099],[-70.688214,45.563981],[-70.659286,45.58688],[-70.644687,45.607083],[-70.592252,45.629865],[-70.5584,45.666671],[-70.525831,45.666551],[-70.469869,45.701639],[-70.438878,45.704387],[-70.400404,45.719834],[-70.383552,45.734869],[-70.388501,45.749717],[-70.406548,45.761813],[-70.417641,45.79377],[-70.395907,45.798885],[-70.39662,45.808486],[-70.387916,45.819043],[-70.34244,45.852192],[-70.306162,45.85974],[-70.259117,45.890755],[-70.253897,45.906524],[-70.263313,45.923832],[-70.240177,45.943729],[-70.26541,45.962692],[-70.31628,45.963113],[-70.307463,45.982541],[-70.284571,45.995384],[-70.303034,45.998976],[-70.317629,46.01908],[-70.278334,46.057019],[-70.284176,46.062758],[-70.310609,46.064544],[-70.284554,46.098713],[-70.254021,46.0996],[-70.255038,46.108348],[-70.237947,46.147378],[-70.278034,46.175001],[-70.292736,46.191599],[-70.272054,46.209833],[-70.248421,46.267072],[-70.232682,46.284428],[-70.205719,46.299865],[-70.203119,46.31438],[-70.208733,46.328961],[-70.191412,46.348072],[-70.174709,46.358472],[-70.148529,46.358923],[-70.129734,46.369384],[-70.125459,46.381352],[-70.11044,46.38611],[-70.096286,46.40943],[-70.057061,46.415036],[-69.997086,46.69523],[-69.22442,47.459686],[-69.203886,47.452203],[-69.178412,47.456615],[-69.146439,47.44886],[-69.082508,47.423976],[-69.061192,47.433052],[-69.043947,47.427634],[-69.036882,47.407977],[-69.045403,47.39191],[-69.039818,47.386309],[-69.053885,47.377878],[-69.054628,47.315911],[-69.049118,47.306471],[-69.052748,47.294403],[-69.047076,47.267089],[-69.050334,47.256621],[-69.033456,47.240984],[-68.966433,47.212712],[-68.96113,47.205582],[-68.942484,47.206386],[-68.920253,47.195048],[-68.919752,47.189859],[-68.902425,47.178839],[-68.857519,47.19095],[-68.812157,47.215461],[-68.764487,47.222331],[-68.717867,47.240919],[-68.705314,47.238066],[-68.687662,47.244215],[-68.664071,47.236762],[-68.619749,47.243218],[-68.595427,47.257698],[-68.59688,47.271731],[-68.578551,47.287551],[-68.553896,47.28225],[-68.517982,47.296092],[-68.474851,47.297534],[-68.448844,47.282547],[-68.378678,47.287561],[-68.376319,47.294257],[-68.384706,47.305094],[-68.380334,47.340242],[-68.355171,47.35707],[-68.329879,47.36023],[-68.303778,47.355524],[-68.284101,47.360389],[-68.265138,47.352543],[-68.234604,47.355035],[-68.214551,47.339637],[-68.15515,47.32542],[-68.152302,47.309878],[-68.137059,47.296068],[-68.082896,47.271921],[-68.074061,47.259764],[-68.019724,47.238036],[-67.991871,47.212042],[-67.955669,47.199542],[-67.935868,47.164843],[-67.893266,47.129943],[-67.881302,47.103913],[-67.790515,47.067921],[-67.781095,45.943032],[-67.777626,45.934207],[-67.750422,45.917898],[-67.763725,45.91043],[-67.767827,45.898568],[-67.803318,45.883718],[-67.803678,45.869379],[-67.796514,45.859961],[-67.755068,45.82367],[-67.780507,45.817622],[-67.801989,45.803546],[-67.806598,45.794723],[-67.806308,45.755405],[-67.793083,45.750559],[-67.781892,45.731189],[-67.809833,45.729274],[-67.803148,45.696127],[-67.817892,45.693705],[-67.803313,45.677886],[-67.768648,45.677581],[-67.754245,45.667791],[-67.720401,45.662522],[-67.71799,45.665243],[-67.73372,45.684233],[-67.734605,45.688987],[-67.729908,45.689012],[-67.710464,45.679372],[-67.675417,45.630959],[-67.64581,45.613597],[-67.640238,45.616178],[-67.644206,45.62322],[-67.639741,45.624771],[-67.606172,45.606533],[-67.499444,45.587014],[-67.488452,45.594643],[-67.491061,45.598917],[-67.455406,45.604665],[-67.429716,45.583773],[-67.420976,45.550029],[-67.425399,45.540795],[-67.432236,45.541023],[-67.435275,45.530781],[-67.432207,45.519996],[-67.416416,45.503515],[-67.462882,45.508691],[-67.470732,45.500067],[-67.503088,45.489688],[-67.499767,45.47805],[-67.482353,45.460825],[-67.484328,45.451955],[-67.473366,45.425328],[-67.430001,45.392965],[-67.418747,45.37726],[-67.434281,45.365438],[-67.427797,45.355471],[-67.434996,45.340133],[-67.456288,45.32644],[-67.452267,45.316839],[-67.460554,45.300379],[-67.466091,45.29416],[-67.485683,45.291433],[-67.489464,45.282653],[-67.46357,45.244097],[-67.453473,45.241127],[-67.43998,45.227047],[-67.428889,45.193213],[-67.407139,45.179425],[-67.404629,45.159926],[-67.383635,45.152259],[-67.345585,45.126392],[-67.294881,45.149666],[-67.302568,45.161348],[-67.291417,45.17145],[-67.290603,45.187589],[-67.283619,45.192022],[-67.246697,45.180765],[-67.242293,45.17224],[-67.227324,45.163652],[-67.203933,45.171407],[-67.157919,45.161004],[-67.112414,45.112323],[-67.090786,45.068721],[-67.105899,45.065786],[-67.117688,45.05673],[-67.082074,45.029608],[-67.068274,45.001014],[-67.05461,44.986764],[-67.033474,44.939923],[-66.984466,44.912557],[-66.990351,44.882551],[-66.978142,44.856963],[-66.996523,44.844654],[-66.986318,44.820657],[-66.975009,44.815495],[-66.952112,44.82007],[-66.950569,44.814539],[-66.961068,44.807269],[-66.979708,44.80736],[-67.02615,44.768199],[-67.04335,44.765071],[-67.05516,44.771442],[-67.062239,44.769543],[-67.073439,44.741957],[-67.098931,44.741311],[-67.103957,44.717444],[-67.128792,44.695421],[-67.139209,44.693849],[-67.155119,44.66944],[-67.169857,44.662105],[-67.186612,44.66265],[-67.192068,44.655515],[-67.189427,44.645533],[-67.234275,44.637201],[-67.251247,44.640825],[-67.274122,44.626345],[-67.27706,44.61795],[-67.273076,44.610873],[-67.293403,44.599265],[-67.314938,44.598215],[-67.32297,44.609394],[-67.310745,44.613212],[-67.293665,44.634316],[-67.292462,44.648455],[-67.309627,44.659316],[-67.307909,44.691295],[-67.300144,44.696752],[-67.299176,44.705705],[-67.308538,44.707454],[-67.355966,44.69906],[-67.376742,44.681852],[-67.381149,44.66947],[-67.367298,44.652472],[-67.363158,44.631825],[-67.377554,44.619757],[-67.386605,44.626974],[-67.405492,44.594236],[-67.428367,44.609136],[-67.457747,44.598014],[-67.492373,44.61795],[-67.493632,44.628863],[-67.505804,44.636837],[-67.522802,44.63306],[-67.530777,44.621938],[-67.543368,44.626554],[-67.551133,44.621938],[-67.575056,44.560659],[-67.562321,44.539435],[-67.568159,44.531117],[-67.648506,44.525403],[-67.660678,44.537575],[-67.685861,44.537155],[-67.702649,44.527922],[-67.698872,44.51575],[-67.71419,44.495238],[-67.733986,44.496252],[-67.743353,44.497418],[-67.742942,44.526453],[-67.753854,44.543661],[-67.774001,44.547438],[-67.779457,44.543661],[-67.781556,44.520577],[-67.79726,44.520685],[-67.808837,44.544081],[-67.839896,44.558771],[-67.845772,44.551636],[-67.843254,44.542822],[-67.856684,44.523934],[-67.851648,44.484901],[-67.868774,44.465272],[-67.868875,44.456881],[-67.851764,44.428695],[-67.855108,44.419434],[-67.868856,44.424672],[-67.878509,44.435585],[-67.887323,44.433066],[-67.899571,44.394078],[-67.913346,44.430128],[-67.926357,44.431807],[-67.931453,44.411848],[-67.955737,44.416278],[-67.961613,44.4125],[-67.961613,44.39907],[-67.978876,44.387034],[-67.985668,44.386917],[-68.000646,44.406624],[-68.010719,44.407464],[-68.019533,44.396971],[-68.01399,44.390255],[-68.034223,44.360456],[-68.044296,44.357938],[-68.043037,44.343667],[-68.049334,44.33073],[-68.067047,44.335692],[-68.076066,44.347925],[-68.077873,44.373047],[-68.086268,44.376405],[-68.092983,44.370949],[-68.11229,44.401588],[-68.119845,44.445658],[-68.117746,44.475038],[-68.150904,44.482383],[-68.17105,44.470211],[-68.194554,44.47189],[-68.189517,44.478605],[-68.192036,44.487419],[-68.213861,44.492456],[-68.223934,44.487],[-68.224354,44.464335],[-68.22939,44.463496],[-68.2445,44.471051],[-68.252474,44.483222],[-68.261708,44.484062],[-68.270522,44.459718],[-68.281015,44.451324],[-68.298223,44.449225],[-68.299063,44.437893],[-68.294865,44.432857],[-68.268423,44.440411],[-68.247438,44.433276],[-68.24366,44.420685],[-68.249956,44.414809],[-68.21554,44.390466],[-68.20354,44.392365],[-68.184532,44.369145],[-68.173608,44.328397],[-68.191924,44.306675],[-68.233435,44.288578],[-68.275139,44.288895],[-68.289409,44.283858],[-68.298223,44.276303],[-68.298643,44.26665],[-68.290818,44.247673],[-68.317588,44.225101],[-68.339498,44.222893],[-68.343132,44.229505],[-68.377982,44.247563],[-68.401268,44.252244],[-68.430946,44.298624],[-68.430853,44.312609],[-68.409027,44.32562],[-68.421619,44.336113],[-68.409867,44.356259],[-68.396552,44.363941],[-68.398035,44.376191],[-68.3581,44.392337],[-68.359082,44.402847],[-68.3791,44.430049],[-68.387678,44.430936],[-68.392559,44.41807],[-68.416412,44.397973],[-68.427874,44.3968],[-68.433901,44.401534],[-68.429648,44.439136],[-68.439281,44.448043],[-68.455095,44.447498],[-68.46382,44.436592],[-68.458849,44.412141],[-68.464106,44.398078],[-68.461072,44.378504],[-68.466109,44.377245],[-68.47828,44.378084],[-68.483317,44.388157],[-68.472824,44.404106],[-68.480379,44.432647],[-68.485415,44.434326],[-68.494649,44.429709],[-68.499686,44.414179],[-68.51452,44.41334],[-68.529905,44.39907],[-68.555088,44.403687],[-68.565161,44.39907],[-68.564741,44.385219],[-68.559285,44.374307],[-68.550051,44.371788],[-68.545434,44.355],[-68.563209,44.333039],[-68.566203,44.313007],[-68.556236,44.300819],[-68.538595,44.299902],[-68.531532,44.290388],[-68.528611,44.276117],[-68.519516,44.265046],[-68.529802,44.249594],[-68.525302,44.227554],[-68.550802,44.236534],[-68.603385,44.27471],[-68.682979,44.299201],[-68.733004,44.328388],[-68.762021,44.329597],[-68.795063,44.30786],[-68.827197,44.31216],[-68.825419,44.334547],[-68.814811,44.362194],[-68.821767,44.40894],[-68.815325,44.42808],[-68.801634,44.434803],[-68.783679,44.473879],[-68.829153,44.462242],[-68.880271,44.428112],[-68.897104,44.450643],[-68.927452,44.448039],[-68.931934,44.43869],[-68.946582,44.429108],[-68.982449,44.426195],[-68.990767,44.415033],[-68.978815,44.38634],[-68.961111,44.375076],[-68.948164,44.355882],[-68.954465,44.32405],[-68.979005,44.296327],[-69.003682,44.294582],[-69.005071,44.274071],[-69.040193,44.233673],[-69.054546,44.171542],[-69.079835,44.160953],[-69.075667,44.129991],[-69.080331,44.117824],[-69.100863,44.104529],[-69.101107,44.093601],[-69.092,44.085734],[-69.050814,44.094888],[-69.031878,44.079036],[-69.048917,44.062506],[-69.056093,44.06949],[-69.067876,44.067596],[-69.079805,44.055256],[-69.073767,44.046135],[-69.125738,44.019623],[-69.124475,44.007419],[-69.170345,43.995637],[-69.193805,43.975543],[-69.19633,43.950504],[-69.203668,43.941806],[-69.259838,43.921427],[-69.267515,43.943667],[-69.280498,43.95744],[-69.31427,43.942951],[-69.319751,43.94487],[-69.304301,43.962068],[-69.331411,43.974311],[-69.351961,43.974748],[-69.366702,43.964755],[-69.388059,43.96434],[-69.398455,43.971804],[-69.421072,43.938261],[-69.423324,43.915507],[-69.459637,43.903316],[-69.483498,43.88028],[-69.50329,43.837673],[-69.514889,43.831298],[-69.513267,43.84479],[-69.520301,43.868498],[-69.524673,43.875639],[-69.543912,43.881615],[-69.54945,43.880012],[-69.545028,43.861241],[-69.552606,43.841347],[-69.572697,43.844012],[-69.578527,43.823316],[-69.588551,43.81836],[-69.604179,43.813551],[-69.604616,43.825793],[-69.592373,43.830895],[-69.589167,43.851299],[-69.594705,43.858878],[-69.604616,43.858004],[-69.621086,43.826814],[-69.634932,43.845907],[-69.649798,43.836287],[-69.653337,43.79103],[-69.664922,43.791033],[-69.685579,43.820546],[-69.705838,43.823024],[-69.714873,43.810264],[-69.719723,43.786685],[-69.752801,43.75594],[-69.780097,43.755397],[-69.778494,43.747089],[-69.835323,43.721125],[-69.838689,43.70514],[-69.851297,43.703581],[-69.855081,43.704746],[-69.858947,43.740531],[-69.868673,43.742701],[-69.862155,43.758962],[-69.869732,43.775656],[-69.884066,43.778035],[-69.903164,43.77239],[-69.927011,43.780174],[-69.948539,43.765948],[-69.958056,43.767786],[-69.982574,43.750801],[-69.992615,43.724793],[-70.001645,43.717666],[-70.006954,43.717065],[-69.998793,43.740385],[-70.001708,43.744466],[-70.041351,43.738053],[-70.034355,43.759041],[-69.99821,43.798684],[-70.002874,43.812093],[-70.011035,43.810927],[-70.026193,43.822587],[-70.023278,43.834247],[-70.002874,43.848239],[-70.009869,43.859315],[-70.019197,43.858733],[-70.064671,43.813259],[-70.06642,43.819672],[-70.080995,43.819672],[-70.107229,43.809178],[-70.142792,43.791688],[-70.153869,43.781194],[-70.153869,43.774781],[-70.176023,43.76079],[-70.17544,43.777113],[-70.190014,43.771866],[-70.197593,43.753211],[-70.194678,43.742134],[-70.217998,43.71998],[-70.216832,43.704822],[-70.23199,43.704822],[-70.251812,43.683251],[-70.254144,43.676839],[-70.242289,43.669544],[-70.240987,43.659132],[-70.211204,43.625765],[-70.217087,43.596717],[-70.214369,43.590445],[-70.20112,43.586515],[-70.196911,43.565146],[-70.206123,43.557627],[-70.231963,43.561118],[-70.244331,43.551849],[-70.261917,43.553687],[-70.272497,43.562616],[-70.307764,43.544315],[-70.353392,43.535405]]]]},\"properties\":{\"name\":\"Maine\",\"nation\":\"USA \"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
The Bi-State Distinct Population Segment (Bi-State DPS) of greater sage-grouse (Centrocercus urophasianus, hereinafter “sage-grouse”) represents a genetically distinct and geographically isolated population that straddles the border between Nevada and California. The primary threat to these sage-grouse populations is the expansion of single-leaf pinyon (Pinus monophylla) and Utah juniper (Juniperus osteosperma) into sagebrush ecosystems, which fragments and reduces population connectivity and survival. Other important threats include low water availability during brood-rearing, particularly during drought, and increased predation by common ravens (Corvus corax), a generalist predator often associated with anthropogenic resource subsidies. Although the Bi-State DPS occurs at high elevations relative to sage-grouse range-wide, changes in historical wildfire cycles and the conversion of native shrubs to invasive annual grasslands still threaten these populations. The Bi-State DPS has undergone multiple federal status assessments and associated litigation. For example, in October of 2013, the Bi-State DPS was proposed for listing as threatened under the Endangered Species Act of 1973 by the U.S. Fish and Wildlife Service (USFWS), then withdrawn in April 2015. The withdrawal decision was challenged, and in May 2018, a Federal district court ordered the withdrawal decision to be vacated, and USFWS was required to re-open the October 2013 listing evaluation.
In response, the U.S. Geological Survey (USGS), with State and Federal collaborators, embarked on a multipronged analysis to provide current and best available science regarding population status of sage-grouse within the Bi-State DPS. Using data from a long-term monitoring program, we carried out four analytical study objectives, and here, we provide preliminary results of these analyses. First, we used integrated population modeling (IPM) to predict annual population abundance and annual finite rate of population change for the Bi-State DPS, as a whole, and for each subpopulation between 1995 and 2018. Because sage-grouse exhibit population cycles (periodic increases and decreases in abundance across approximately 6- to 10-year wavelengths), we estimated trends across three nested temporal scales that represent one (11 years), two (18 years), and three (24 years) complete population cycles. These estimates of relatively long-term averaged population change account for temporal (that is, interannual) variation. Our model predicted population abundance for the Bi-State DPS during 2018 at 3,305 individuals (2,247–4,683), with the majority occupying Bodie Hills and Long Valley. The model also predicted cyclic dynamics in abundance through time with evidence of 24-year population growth and slight trends of decline over the past 18 years. Specifically, across the Bi-State DPS as a whole, we estimated annual average at 0.99, 0.99, and 1.02 over the one, two, and three population cycles, which equated to a 10.5 percent, 16.6 percent decrease, and 60.0 percent increase in abundance over the 11-, 18-, and 24-year cycles. Estimated abundance in 2018 had not reached numbers lower than those predicted during 1995. However, we found spatial variation in population trends across the three cycles. Bodie Hills subpopulation comprised the greatest (1,521) and exhibited average annual greater than 1.0 across all periods resulting in average annual increases of 7 percent. This relatively large subpopulation has grown 5 times larger than what was predicted in 1995 while experiencing cyclical dynamics within that period.
Conversely, other smaller subpopulations within the Bi-State DPS exhibited average annual equal to or less than 1.0 resulting in estimated 10-year risks of extirpation ranging from 2.0 to 76.1 percent. In general, evidence of decline among smaller subpopulations was greatest for the most recent period (2008–18) compared to a period that encompassed three full population cycles (24-year). This difference coincides with an intense period of drought that began in 2012.
For comparative purposes as part of this first objective, we conducted a similar analysis for populations of sage-grouse within Nevada and California but outside the Bi-State DPS. We developed a region-wide and distance-weighted IPM using lek count from Nevada Department of Wildlife (NDOW) and California Department of Fish and Wildlife (CDFW) databases and with telemetry data collected by USGS across 12 sage-grouse subpopulations. Our models predicted similar patterns in population cycling outside the Bi-State DPS but with much stronger evidence of long-term declines across 24 years. Specifically, median averaged across each year of the 11-, 18-, and 24-year periods resulted in average annual values of 0.94, 0.97, and 0.99, respectively. These values equate to 41.0 percent, 38.5 percent, and 21.3 percent declines over the corresponding periods.
Second, we used lek count data in a state-space modeling framework to compare trends in population abundance across different spatial scales (that is, leks versus Bi-State DPS). This hierarchical framework allowed us to disentangle declines associated with climate conditions as opposed to other local level factors that might signal the need for management intervention. Specifically, we identified 7 leks that were both declining and recently decoupled from larger spatial scale trends, typically governed by climatic conditions (referred to as soft or hard signals). The goal of this analysis was to provide an early warning system that might have implications for conservation actions at local scales.
Third, we developed phenological (spring, summer–fall, and winter) and reproductive life stage (nesting, early brood-rearing, and late-brood rearing) based resource selection functions using various environmental covariates. We report rankings of variable importance for each season and life stage, developed maps of habitat selection indices (HSI), binned categories representing low, moderate, and high classes of quality (where any category greater than or equal to low indicated selected habitat) for each phenological season and life stage, and produced composite maps by selected phenological and reproductive stage to estimate annual habitat.
Fourth, we used for each lek within the Bi-State DPS to carry out a spatial analysis that quantified substantial changes in the distribution of occupied habitat across long- (24-year) and short- (11-year) term periods. Owing to differences among available datasets, the long-term analysis primarily reflected spatial shifts among subpopulations comprising the majority of the Bi-State DPS (that is, Bodie Hills and Long Valley) while the short-term analysis also quantified changes among subpopulations along the periphery. Over long and short-term periods, the overall distribution of occupied habitat (as measured by 99 percent utilization distributions intersecting any quantified habitat) was reduced by 20,573 ha and 55,492 ha, respectively. Occupied core areas (as measured by 50 percent utilization distributions intersecting any quantified habitat) over long-term periods were solely located in Bodie Hills and Long Valley. Although nearly all subpopulations experienced contractions in occupied overall and core distribution, Bodie Hills experienced spatial expansion that occurred with concomitant spatial contraction at Long Valley over both periods. Subpopulations at the northern (Pine Nuts), central (Sagehen) and southern (White-Mountains) extents of the Bi-State DPS also experienced spatial contraction over the short-term period. These findings, coupled with those of population trends, indicate long-term patterns in redistribution of sage-grouse from Long Valley and peripheral subpopulations to Bodie Hills. That is, sage-grouse subpopulations at the periphery are declining while the largest population at the core is increasing, which could have meaningful impacts on overall metapopulation persistence. We provide evidence for loss of occupied habitat (reduced distribution) given local extirpation of subpopulations.
Fifth, we calculated percentages of selected phenological, life stage, and annual habitat that each subpopulation contributed to the Bi-State DPS. We then intersected these maps with a composite estimate of occupied habitat from the fourth objective and calculated percentages of selected habitat likely occupied by sage-grouse that each subpopulation contributed to the Bi-State DPS. These values provide evidence for loss of occupied habitat and subsequent reductions in spatial distribution given reductions in abundance and, in some cases, extirpation of leks within subpopulations.
Lastly, we carried out an initial in-depth analysis of selection for irrigated pastures and wet meadows during the brood-rearing stage for the Long Valley subpopulation. We chose this subpopulation because it represents a population core, representing 26.5 percent of total sage-grouse within the Bi-State DPS, and has exhibited long-term declines in abundance and distribution. This subpopulation is highly sensitive to precipitation and other factors that influence water availability. Models predicted higher use of the interior portions of irrigated pastures and wet meadows during late brood-rearing period, which represented a potentially risky use of habitat that was exacerbated during periods of low moisture (for example, drought, reduced water delivery, or both). Sage-grouse typically used edges of riparian areas and pastures, largely because the interior of these mesic areas consisted of considerably less overhead concealment cover (for example, shrubs) that likely resulted in a higher risk of mortality. We found that a lack of water delivery to pastures in the form of overwinter precipitation or diversion ditches increased the movements of sage-grouse to the interior of pastures. Although further investigation of water delivery impacts on chick survival are warrented, our initial findings regarding resource selection may explain recent declines in population growth at Long Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191149","collaboration":"Prepared in cooperation with the U. S. Fish and Wildlife Service, Bureau of Land Management, California Department of Fish and Wildlife, Nevada Department of Wildlife, and the U.S. Forest Service","usgsCitation":"Coates, P.S., Ricca, M.A., Prochazka, B.G., O’Neil, S,T., Severson, J.P., Mathews, S.R., Espinosa, S., Gardner, S., Lisius, S., and Delehanty, D.J., 2020, Population and habitat analyses for greater sage-grouse (Centrocercus urophasianus) in the bi-state distinct population segment—2018 update: U.S. Geological Survey Open-File Report 2019–1149, 122 p., https://doi.org/10.3133/ofr20191149.","productDescription":"x, 122 p.","onlineOnly":"Y","ipdsId":"IP-113768","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":371329,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1149/ofr20191149.pdf","text":"Report","size":"8.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1149"},{"id":371330,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1149/ofr20191149_hirez.pdf","text":"Report — High resolution graphics","size":"82.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1149 Hi Rez"},{"id":371328,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1149/coverthb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.234375,\n 38.90813299596705\n ],\n [\n -119.92675781249999,\n 38.436379603\n ],\n [\n -119.72900390625001,\n 37.50972584293751\n ],\n [\n -118.85009765625,\n 36.90597988519294\n ],\n [\n -117.83935546874999,\n 36.12900165569652\n ],\n [\n -117.586669921875,\n 36.1733569352216\n ],\n [\n -117.31201171875001,\n 35.862343734896484\n ],\n [\n -116.806640625,\n 36.075742215627\n ],\n [\n -115.224609375,\n 35.79108281624994\n ],\n [\n -115.587158203125,\n 37.125286284966805\n ],\n [\n -117.23510742187501,\n 38.62545397209084\n ],\n [\n -119.5751953125,\n 39.16414104768742\n ],\n [\n -120.234375,\n 38.90813299596705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Multiple aquifer tests were conducted in Lovelock, Nevada, to determine hydraulic conductivity and storage properties to be used with the numerical groundwater flow model of the lower Humboldt River Basin while accounting for the influence of surface features with a modeling component. The numerical model will ultimately provide the Nevada Division of Water Resources (NDWR) with information regarding the impacts of groundwater pumping on the Humboldt River, allowing the Nevada State Engineer to make informed decisions in the conjunctive management of the State’s groundwater and surface water resources. Seven slug tests, one single-well pumping test, and two multi-well pumping tests were conducted to evaluate properties of shallow Lahontan clays and silts; shallow fluvial deposits; and coarser, water-bearing deposits of the younger alluvium. Aquifer tests were conducted between March 2017 and April 2018. Results indicate aquifers in the Lovelock Valley have transmissivity ranging between 0.0001 and 95,000 feet squared per day (ft2/day). The results of these tests provide constraints on hydraulic properties for a numerical groundwater flow model being developed for a capture study in the lower Humboldt River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191133","collaboration":"Prepared in cooperation with the Nevada Division of Water Resources","usgsCitation":"Nadler, C., 2020, Analysis of aquifer framework and hydraulic properties of Lovelock Valley, Lovelock, Nevada: U.S. Geological Survey Open-File Report 2019–1133, 48 p., https://doi.org/10.3133/ofr20191133.","productDescription":"Report: viii, 48 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-098941","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":399425,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109593.htm"},{"id":371146,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1133/ofr20191133.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"}},{"id":371147,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LIL7PZ","linkHelpText":"Supplemental data for analysis of aquifer framework and hydraulic properties of Lovelock Valley, Lovelock, NV"},{"id":371145,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1133/coverthb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Humboldt River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.597412109375,\n 39.14710270770074\n ],\n [\n -114.70825195312501,\n 39.14710270770074\n ],\n [\n -114.70825195312501,\n 41.91045347666418\n ],\n [\n -118.597412109375,\n 41.91045347666418\n ],\n [\n -118.597412109375,\n 39.14710270770074\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Repeated sampling and grain-size analysis of surficial sediments along the sandy shorelines of Louisiana is necessary to characterize coastal-zone sediment properties and evaluate sediment transport patterns within the nearshore environments. In 2008, and again in 2015 and 2016, sediment grab samples were collected along the shorelines of the western Chenier plain, the Isles Dernieres (Raccoon, Whiskey, Trinity and East Islands), the Lafourche delta (Timbalier Islands, Caminada Headland, and Grand Isle), the modern delta (Grand Terre Islands from Chaland Headland to Sandy Point), and the Chandeleur Islands (from Curlew Island to Hewes Point). The samples were collected as part of the Louisiana Coastal Protection and Restoration Authority (CPRA) Barrier Island Comprehensive Monitoring (BICM) Program in collaboration with the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS–SPCMSC) and the University of New Orleans Pontchartrain Institute for Environmental Studies (UNO–PIES). Physical properties of the samples (sediment grain size and sorting) were measured and provided in data reports to CPRA. Additional samples collected by the USGS from around Breton Island in 2014 and 2015 supplemented the 2015–2016 BICM data to complete the coastwide dataset. This report compares the results of the 2008 and 2015–2016 sedimentologic analyses and documents changes in composition (percent sand) and mean sediment grain size between the two time periods. At most sample sites, differences in mean grain size varied by less than ±0.25 Φ. The largest changes occurred at sites located near tidal inlets or along rapidly eroding shorelines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191132","collaboration":"Prepared in cooperation with the University of New Orleans and Louisiana Coastal Protection and Restoration Authority","usgsCitation":"Bosse, S.T., Flocks, J.G., Bernier, J.C., Georgiou, I.Y., Kulp, M.A., and Brown, M., 2019, Louisiana Coastal Zone sediment characterization; comparison of sediment grain sizes for samples collected in 2008 and 2015–2016 from the western Chenier plain to the Chandeleur Islands, Louisiana—Louisiana Barrier Island Comprehensive Monitoring (BICM) Program: U.S. Geological Survey Open-File Report 2019–1132, 17 p., https://doi.org/10.3133/ofr20191132.","productDescription":"vi, 17 p.","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-107806","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":371101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1132/report-thumb.jpg"},{"id":371102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1132/ofr20191132.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1132"},{"id":399424,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109589.htm"},{"id":399423,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109588.htm"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.8333,\n 29.6667\n ],\n [\n -93,\n 29.6667\n ],\n [\n -93,\n 29.7889\n ],\n [\n -93.8333,\n 29.7889\n ],\n [\n -93.8333,\n 29.6667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
The United States Geological Survey (USGS) provided technical support to the Washington Department of Ecology (Ecology) in their assessment of the role groundwater plays in contributing pollutant loading to the Green-Duwamish River near Seattle, Washington. Ecology is developing watershed hydrology models of the Green-Duwamish watershed, and need to assign realistic contaminant concentrations to the various Hydrologic Response Units represented in their models. The USGS compiled existing groundwater quality data in the Green-Duwamish watershed, and this report summarizes results and interpretation of the dataset, including identifying data gaps and needs for further research and monitoring. The sources of existing data were the USGS’s National Water Information System, Ecology’s Environmental Information Management System, and a compilation of several studies by Leidos, a scientific research company. The water-quality parameters of interest included polychlorinated biphenyl (PCB) Aroclors and congeners, phthalates, carcinogenic polycyclic aromatic hydrocarbons (cPAHs), arsenic, copper, and zinc. Results were grouped into the four subwatersheds delineated in Ecology’s hydrology models: Duwamish, Lower Green, Soos, and Upper Green. Results from the Duwamish subwatershed were further sub-divided by the USGS into the Lower Duwamish, containing land adjacent to the Lower Duwamish Waterway Superfund site, and the Upper Duwamish, containing the remaining area of the Duwamish subwatershed. Groundwater quality data in the Lower Duwamish were treated separately because there is known contamination in this area. The availability of water quality data varied by subwatershed as follows: phthalate data was only available within the Duwamish, PCB data was available within the Duwamish and Lower Green, cPAH data was available within the Duwamish, Lower Green, and Soos, and data for arsenic, copper, and zinc were available within all four subwatersheds. More than 99 percent of the available data was within the Duwamish subwatershed, identifying a need for additional monitoring of groundwater quality in the other subwatersheds.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191131","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Senter, C.A., Conn, K.E., Black, R.W., Welch, W.B., and Fasser, E.T., 2020, Assessment of existing groundwater quality data in the Green-Duwamish watershed, Washington: U.S. Geological Survey Open-File Report 2019-1131, 35 p., https://doi.org/10.3133/ofr20191131.","productDescription":"iv, 35 p.","onlineOnly":"Y","ipdsId":"IP-111911","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":371094,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1131/coverthb2.jpg"},{"id":371095,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1131/ofr20191131.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1131"},{"id":399422,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109587.htm"}],"country":"United States","state":"Washington","otherGeospatial":"Green-Duwamish watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.26684570312499,\n 47.59505101193038\n ],\n [\n -122.420654296875,\n 47.60245929546312\n ],\n [\n -122.43713378906249,\n 47.51349065484327\n ],\n [\n -122.431640625,\n 47.32393057095941\n ],\n [\n -122.420654296875,\n 47.18597932702905\n ],\n [\n -122.3876953125,\n 47.010225655683485\n ],\n [\n -122.288818359375,\n 46.912750956378915\n ],\n [\n -122.2283935546875,\n 46.781254534638606\n ],\n [\n -122.0855712890625,\n 46.558860303117164\n ],\n [\n -121.59667968749999,\n 46.32417161725691\n ],\n [\n -121.39892578125,\n 46.32796494040746\n ],\n [\n -121.30004882812499,\n 46.49839225859763\n ],\n [\n -121.168212890625,\n 46.78501604269254\n ],\n [\n -121.36596679687499,\n 46.991494313050424\n ],\n [\n -121.95922851562501,\n 47.4355191531953\n ],\n [\n -122.26684570312499,\n 47.59505101193038\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Substantial effort has been dedicated to developing reliable monitoring schemes for North American bird populations, but our ability to monitor bird populations in the boreal forest remains limited because of the sparsity of long-term data sets, particularly in northerly regions. Given the importance of the boreal forest for many migratory birds, we set out to (1) summarize the main challenges associated with monitoring avian populations, (2) describe the available statistical tools for population monitoring and their applications, and (3) identify future directions to overcome current challenges in monitoring bird populations in the boreal forest. Defining and delineating populations of interest and identifying the drivers that affect those populations present the greatest current challenges. This is because migratory birds may be affected by many population-limiting processes at different stages of their annual life cycles. These factors are often hierarchically structured and can influence populations at the local, regional, or continental scales. Some of the challenges associated with delineating populations and identifying population drivers can be addressed via the plethora of sampling and analytic methods available to examine population change over time. Choosing the proper analytic methods depends on the goals of the study and the nature of the data such as single or multiple populations, repeated occurrence or count-based surveys, or demographic rates. Recent advances in hierarchical and integrated population models make these analytic approaches some of the most promising avenues for the development of future methods. However, these tools require large data sets, and acquiring sufficient data on bird populations and potential explanatory variables is difficult in the boreal forest. If the current challenges to monitoring birds in the boreal forest are to be overcome, serious effort should be dedicated to integrating existing data and making them accessible. Enhancing survey effort through multispecies surveys will also play an important role. Implementing spatially balanced sampling plans with a rotating panel design could balance the trade-offs between spatial versus temporal replication at an affordable cost. Improving the accessibility of environmental covariates that are spatially and temporally explicit would also enable development of mechanistic population models that improve our understanding of migratory bird population dynamics. Finally, given that long-term monitoring programs can take many decades before delivering reliable population trends and that organizational priorities often change over time, we suggest that collaborative efforts will help ensure the long-term survival of new monitoring programs.
","language":"English","publisher":"Resilience Alliance Publications","doi":"10.5751/ACE-01397-140208","usgsCitation":"Roy, C., Michel, N.L., Handel, C.M., Van Wilgenburg, S., Burkhalter, C., Gurney, K.A., Messmer, D., Prince, K., Rushing, C.S., Saracco, J.E., Schuster, R., Smith, A.C., Smith, P.A., Solymos, P., Venier, L.A., and Zuckerberg, B., 2019, Monitoring boreal avian populations: How can we estimate trends and trajectories from noisy data?: Avian Conservation and Ecology, v. 14, no. 2, 8, 26 p., https://doi.org/10.5751/ACE-01397-140208.","productDescription":"8, 26 p.","ipdsId":"IP-095185","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":458858,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/ace-01397-140208","text":"Publisher Index Page"},{"id":377117,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.630859375,\n 32.62087018318113\n ],\n [\n -101.6015625,\n 31.653381399664\n ],\n [\n -96.94335937499999,\n 29.76437737516313\n ],\n [\n -87.451171875,\n 29.99300228455108\n ],\n [\n -82.44140625,\n 28.613459424004414\n ],\n [\n -81.650390625,\n 26.745610382199022\n ],\n [\n -79.365234375,\n 26.667095801104814\n ],\n [\n -80.595703125,\n 31.12819929911196\n ],\n [\n -62.9296875,\n 43.70759350405294\n ],\n [\n -49.658203125,\n 47.338822694822\n ],\n [\n -64.248046875,\n 60.75915950226991\n ],\n [\n -78.3984375,\n 56.022948079627454\n ],\n [\n -79.365234375,\n 54.16243396806779\n ],\n [\n -79.62890625,\n 51.56341232867588\n ],\n [\n -82.265625,\n 55.1286490684888\n ],\n [\n -122.51953124999999,\n 69.8698915662856\n ],\n [\n -131.748046875,\n 70.17020068549206\n ],\n [\n -140.2734375,\n 69.59589006237648\n ],\n [\n -163.125,\n 69.62651016802958\n ],\n [\n -165.849609375,\n 68.9110048456202\n ],\n [\n -166.46484375,\n 68.17155518732503\n ],\n [\n -161.982421875,\n 66.5482634621744\n ],\n [\n -161.89453125,\n 66.23145747862573\n ],\n [\n -162.7734375,\n 66.08936427047088\n ],\n [\n -163.65234374999997,\n 66.23145747862573\n ],\n [\n -163.740234375,\n 66.47820814385636\n ],\n [\n -166.640625,\n 66.05371622067922\n ],\n [\n -168.3984375,\n 65.5129625532949\n ],\n [\n -165.849609375,\n 64.4348920430406\n ],\n [\n -166.2890625,\n 60.75915950226991\n ],\n [\n -164.267578125,\n 54.67383096593114\n ],\n [\n -152.9296875,\n 56.607885465009254\n ],\n [\n -146.337890625,\n 59.88893689676585\n ],\n [\n -137.900390625,\n 57.70414723434193\n ],\n [\n -131.484375,\n 51.67255514839674\n ],\n [\n -125.595703125,\n 48.28319289548349\n ],\n [\n -125.33203125,\n 43.51668853502906\n ],\n [\n -124.8046875,\n 40.78054143186033\n ],\n [\n -121.201171875,\n 34.379712580462204\n ],\n [\n -116.630859375,\n 32.62087018318113\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roy, Christian","contributorId":237014,"corporation":false,"usgs":false,"family":"Roy","given":"Christian","email":"","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":794987,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Michel, Nicole L","contributorId":237015,"corporation":false,"usgs":false,"family":"Michel","given":"Nicole","email":"","middleInitial":"L","affiliations":[{"id":27800,"text":"National Audubon Society","active":true,"usgs":false}],"preferred":false,"id":794988,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Handel, Colleen M. 0000-0002-0267-7408 cmhandel@usgs.gov","orcid":"https://orcid.org/0000-0002-0267-7408","contributorId":3067,"corporation":false,"usgs":true,"family":"Handel","given":"Colleen","email":"cmhandel@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":794989,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Wilgenburg, Steven","contributorId":237016,"corporation":false,"usgs":false,"family":"Van Wilgenburg","given":"Steven","email":"","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":794990,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burkhalter, Curtis","contributorId":237017,"corporation":false,"usgs":false,"family":"Burkhalter","given":"Curtis","email":"","affiliations":[{"id":27800,"text":"National Audubon Society","active":true,"usgs":false}],"preferred":false,"id":794991,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gurney, Kirsty A B","contributorId":237018,"corporation":false,"usgs":false,"family":"Gurney","given":"Kirsty","email":"","middleInitial":"A B","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":794992,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Messmer, David","contributorId":237019,"corporation":false,"usgs":false,"family":"Messmer","given":"David","email":"","affiliations":[{"id":47588,"text":"University of Saskatchewan at Saskatoon","active":true,"usgs":false}],"preferred":false,"id":794993,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prince, Karine","contributorId":202981,"corporation":false,"usgs":false,"family":"Prince","given":"Karine","email":"","affiliations":[{"id":36568,"text":"Paris-Sorbonne Univeristy","active":true,"usgs":false}],"preferred":false,"id":794994,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rushing, Clark S","contributorId":237020,"corporation":false,"usgs":false,"family":"Rushing","given":"Clark","email":"","middleInitial":"S","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":794995,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Saracco, James E","contributorId":237021,"corporation":false,"usgs":false,"family":"Saracco","given":"James","email":"","middleInitial":"E","affiliations":[{"id":34260,"text":"Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":794996,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Schuster, Richard","contributorId":237022,"corporation":false,"usgs":false,"family":"Schuster","given":"Richard","email":"","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":794997,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Smith, Adam C.","contributorId":195234,"corporation":false,"usgs":false,"family":"Smith","given":"Adam","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":794998,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Smith, Paul A 0000-0001-9573-5218","orcid":"https://orcid.org/0000-0001-9573-5218","contributorId":216272,"corporation":false,"usgs":false,"family":"Smith","given":"Paul","email":"","middleInitial":"A","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":794999,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Solymos, Peter","contributorId":140674,"corporation":false,"usgs":false,"family":"Solymos","given":"Peter","affiliations":[],"preferred":false,"id":795000,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Venier, Lisa A","contributorId":237023,"corporation":false,"usgs":false,"family":"Venier","given":"Lisa","email":"","middleInitial":"A","affiliations":[{"id":7219,"text":"Natural Resources Canada","active":true,"usgs":false}],"preferred":false,"id":795001,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Zuckerberg, Benjamin","contributorId":200298,"corporation":false,"usgs":false,"family":"Zuckerberg","given":"Benjamin","email":"","affiliations":[{"id":13562,"text":"University of Wisconsin, Madison","active":true,"usgs":false}],"preferred":false,"id":795002,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70203959,"text":"70203959 - 2019 - Element cycling in the Middle-Late Triassic Shublik Formation: Mineralization vs. recycling of biolimiting nutrients in an unconventional resource play","interactions":[],"lastModifiedDate":"2020-05-28T19:05:23.128561","indexId":"70203959","displayToPublicDate":"2019-12-31T14:04:37","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Element cycling in the Middle-Late Triassic Shublik Formation: Mineralization vs. recycling of biolimiting nutrients in an unconventional resource play","docAbstract":"The Triassic Shublik Formation in northern Alaska is one of the major source rocks in North America, having generated much of the petroleum in Prudhoe Bay and associated fields. The middle Shublik Formation, the focus of this study, is a highly phosphatic, organic-rich carbonate mudstone interval. Apatite cements can occur as phosphatic peloids, steinkerns, elongate or angular nodules, and shells or shell fragments. We propose a model whereby phosphatization is favored in early diagenetic environments that have low concentrations of dissolved iron relative to reactive organic matter in the pore water sulfate reduction zone.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"New directions in geosciences for unconventional resources","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"New Directions in Geosciences for Unconventional Resources","conferenceDate":"October 15-17, 2019","conferenceLocation":"Banff, Canada","language":"English","publisher":"CSPG","usgsCitation":"Whidden, K.J., Dumoulin, J.A., Macquaker, J., Birdwell, J.E., Boehlke, A., and French, K.L., 2019, Element cycling in the Middle-Late Triassic Shublik Formation: Mineralization vs. recycling of biolimiting nutrients in an unconventional resource play, in New directions in geosciences for unconventional resources, Banff, Canada, October 15-17, 2019, 11 p.","productDescription":"11 p.","ipdsId":"IP-108677","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":375132,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":364996,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cspg.org/Conferences/Conference_Archives/Gussow_Conferences/Conferences/Gussow/Gussow_Archive/Gussow-Archives.aspx?hkey=c92a974d-b253-4d2e-b6df-88e61db23877"}],"country":"United States","state":"Alaska","otherGeospatial":"National Petroleum Reserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.509765625,\n 67.13582938531948\n ],\n [\n -150.029296875,\n 67.13582938531948\n ],\n [\n -150.029296875,\n 71.35706654962706\n ],\n [\n -162.509765625,\n 71.35706654962706\n ],\n [\n -162.509765625,\n 67.13582938531948\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Whidden, Katherine J. 0000-0002-7841-2553 kwhidden@usgs.gov","orcid":"https://orcid.org/0000-0002-7841-2553","contributorId":3960,"corporation":false,"usgs":true,"family":"Whidden","given":"Katherine","email":"kwhidden@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":764975,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dumoulin, Julie A. 0000-0003-1754-1287 dumoulin@usgs.gov","orcid":"https://orcid.org/0000-0003-1754-1287","contributorId":203209,"corporation":false,"usgs":true,"family":"Dumoulin","given":"Julie","email":"dumoulin@usgs.gov","middleInitial":"A.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":764976,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Macquaker, James","contributorId":216534,"corporation":false,"usgs":false,"family":"Macquaker","given":"James","email":"","affiliations":[{"id":39472,"text":"ExxonMobil","active":true,"usgs":false}],"preferred":false,"id":764977,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":764978,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boehlke, Adam 0000-0003-4980-431X aboehlke@usgs.gov","orcid":"https://orcid.org/0000-0003-4980-431X","contributorId":3470,"corporation":false,"usgs":true,"family":"Boehlke","given":"Adam","email":"aboehlke@usgs.gov","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":789929,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"French, Katherine L. 0000-0002-0153-8035","orcid":"https://orcid.org/0000-0002-0153-8035","contributorId":205462,"corporation":false,"usgs":true,"family":"French","given":"Katherine","email":"","middleInitial":"L.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":false,"id":764979,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215873,"text":"70215873 - 2019 - Applying circuit theory and landscape linkage maps to reintroduction planning for California condors","interactions":[],"lastModifiedDate":"2020-11-02T12:50:26.995399","indexId":"70215873","displayToPublicDate":"2019-12-31T12:56:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Applying circuit theory and landscape linkage maps to reintroduction planning for California condors","docAbstract":"Conservation practitioners are increasingly looking to species translocations as a tool to recover imperiled taxa. Quantitative predictions of where animals are likely to move when released into new areas would allow managers to better address the social, institutional, and ecological dimensions of conservation translocations. Using >5 million California condor (Gymnogyps californianus) occurrence locations from 75 individuals, we developed and tested circuit-based models to predict condor movement away from release sites. We found that circuit-based models of electrical current were well calibrated to the distribution of condor movement data in southern and central California (continuous Boyce Index = 0.86 and 0.98, respectively). Model calibration was improved in southern California when additional nodes were added to the circuit to account for nesting and feeding areas, where condor movement densities were higher (continuous Boyce Index = 0.95). Circuit-based projections of electrical current around a proposed release site in northern California comported with the condor’s historical distribution and revealed that, initially, condor movements would likely be most concentrated in northwestern California and southwest Oregon. Landscape linkage maps, which incorporate information on landscape resistance, complement circuit-based models and aid in the identification of specific avenues for population connectivity or areas where movement between populations may be constrained. We found landscape linkages in the Coast Range and the Sierra Nevada provided the most connectivity to a proposed reintroduction site in northern California. Our methods are applicable to conservation translocations for other species and are flexible, allowing researchers to develop multiple competing hypotheses when there are uncertainties about landscape or social attractants, or uncertainties in the landscape conductance surface.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0226491","usgsCitation":"D’Elia, J., Brandt, J., Burnett, L., Haig, S.M., Hollenbeck, J.P., Kirkland, S., Marcot, B.G., Punzalan, A., West, C.J., Williams-Claussen, T., Wolstenholme, R., and Young, R., 2019, Applying circuit theory and landscape linkage maps to reintroduction planning for California condors: PLoS ONE, v. 14, no. 12, e0226491, 22 p., https://doi.org/10.1371/journal.pone.0226491.","productDescription":"e0226491, 22 p.","ipdsId":"IP-115028","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":458861,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0226491","text":"Publisher Index Page"},{"id":379985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Central California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.68505859375,\n 37.96152331396614\n ],\n [\n -122.03613281249999,\n 36.96744946416934\n ],\n [\n -121.88232421875,\n 36.19109202182454\n ],\n [\n -120.4541015625,\n 34.43409789359469\n ],\n [\n -118.6083984375,\n 33.96158628979907\n ],\n [\n -118.0810546875,\n 34.03445260967645\n ],\n [\n -116.56494140625001,\n 35.24561909420681\n ],\n [\n -119.68505859375,\n 37.96152331396614\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-31","publicationStatus":"PW","contributors":{"authors":[{"text":"D’Elia, Jesse 0000-0002-1843-8495","orcid":"https://orcid.org/0000-0002-1843-8495","contributorId":244237,"corporation":false,"usgs":false,"family":"D’Elia","given":"Jesse","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":803555,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brandt, Joe","contributorId":127746,"corporation":false,"usgs":false,"family":"Brandt","given":"Joe","email":"","affiliations":[{"id":7133,"text":"California Condor Recovery Program, US Fish and Wildlife Service, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":803556,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burnett, LJ","contributorId":244238,"corporation":false,"usgs":false,"family":"Burnett","given":"LJ","email":"","affiliations":[{"id":48872,"text":"Ventana Wildlife Society","active":true,"usgs":false}],"preferred":false,"id":803557,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":803558,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hollenbeck, Jeffrey P","contributorId":244239,"corporation":false,"usgs":false,"family":"Hollenbeck","given":"Jeffrey","email":"","middleInitial":"P","affiliations":[{"id":48873,"text":"The Northwest Habitat Institute","active":true,"usgs":false}],"preferred":false,"id":803559,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kirkland, S","contributorId":244240,"corporation":false,"usgs":false,"family":"Kirkland","given":"S","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":803560,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marcot, B G","contributorId":244241,"corporation":false,"usgs":false,"family":"Marcot","given":"B","email":"","middleInitial":"G","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":803561,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Punzalan, A","contributorId":244242,"corporation":false,"usgs":false,"family":"Punzalan","given":"A","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":803562,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"West, C J","contributorId":244243,"corporation":false,"usgs":false,"family":"West","given":"C","email":"","middleInitial":"J","affiliations":[{"id":38097,"text":"Yurok Tribe","active":true,"usgs":false}],"preferred":false,"id":803563,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Williams-Claussen, T","contributorId":244244,"corporation":false,"usgs":false,"family":"Williams-Claussen","given":"T","email":"","affiliations":[{"id":48874,"text":"Yurok Tribe, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":803564,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Wolstenholme, Rachel","contributorId":206084,"corporation":false,"usgs":false,"family":"Wolstenholme","given":"Rachel","email":"","affiliations":[{"id":37236,"text":"Pinnacles National Park","active":true,"usgs":false}],"preferred":false,"id":803565,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Young, Richard","contributorId":202719,"corporation":false,"usgs":false,"family":"Young","given":"Richard","affiliations":[],"preferred":false,"id":803566,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ]}