The city of Summerset is a growing community in west South Dakota. The Sun Valley Estates subdivision in the north part of the city was developed on unconsolidated deposits surrounded by steep terrain. During years with greater than normal precipitation, particularly in 2019, groundwater levels increased in the unconsolidated deposits and caused damage to stormwater systems, sewer infrastructure, and houses with basements. The U.S. Geological Survey, in cooperation with the City of Summerset, completed a study of the hydrogeology and groundwater flow in the alluvial aquifer part of the unconsolidated deposits in north Summerset to understand the groundwater system in the area and to provide hydrogeologic information in support of future development planning.
The study area included most of the Sun Valley Estates subdivision in the north part of the city of Summerset in the east Black Hills of west South Dakota. About 0.7 square mile of water-bearing alluvial deposits is included in the study area. Precipitation in the study area from 2017 to 2019 was compared to the monthly normal values at a nearby climate site. The largest departure from normal was in May 2019 with precipitation exceeding the monthly normal by about 5 inches (in.). All months in 2019, except March, exceeded the monthly normal precipitation. Cumulative departure from normal precipitation in 2019 increased from about 4 in. greater than normal in January to about 18 in. greater than normal in December.
The geologic setting of the study area is characterized by the surrounding Black Hills. Unconsolidated Quaternary-age deposits overlie consolidated to partially consolidated Mesozoic-age and Paleozoic-age shales, sandstones, and limestones. Surficial deposits of alluvium and other unconsolidated deposits are the primary surficial geologic units in the study area and form the components of the alluvium hydrogeologic unit of the study area. Results from previous studies of alluvium along nearby Rapid Creek estimated hydraulic conductivity to range from 89 to 2,292 feet per day (ft/d), transmissivity to range from 1,001 to 32,083 feet squared per day, and storage coefficients to range from 0.0002 to 0.16. Hydraulic conductivity and transmissivity generally decreased downstream along Rapid Creek (west to east). Slug tests were completed August 16, 2019, at two observation wells completed in the alluvial aquifer in the Sun Valley Estates subdivision to determine hydraulic conductivity. Hydraulic conductivity estimated from AQTESOLV curve-fitting analysis using the Bouwer-Rice method for all slug-in and slug-out trials from two observation wells in the study ranged from 0.20 to 0.26 ft/d for well 441318103220001 (SunValley1 well) and from 0.54 to 14 ft/d for well 441319103215701 (SunValley2 well). The mean, median, and standard deviation of all trials at both wells were 4.3 ft/d, 0.8 ft/d, and 5.6 ft/d, respectively.
The extent of the alluvial aquifer was determined by geologic maps and lithologic logs. Alluvial deposits in the study area extend to about 1 mile in the north–south direction and about 1.5 miles in the southeast–northwest direction. The direction of groundwater flow was estimated using water-level records and topographic maps. The resulting potentiometric map indicated that groundwater in the alluvial aquifer under the Sun Valley Estates subdivision originates from higher elevations of the west part of the area of interest and from streams in the southeast part. Recharge and evapotranspiration estimates were results from a Soil-Water Balance model that calculated a matrix of recharge for 2019 with values ranging from 0 to 11.4 in. and an annual mean value of 5.1 in. across the study area. Soil-Water Balance-estimated potential evapotranspiration for 2019 ranged from 28.90 to 28.75 in. and the estimated annual mean was 28.86 in. across the study area. Estimated groundwater budget components for the alluvial aquifer in the area of interest included inflows and outflows. Total estimated groundwater budget components for inflows for 2019 were about 66 percent from recharge, 33 percent from streamflow, and 1 percent from inflow from adjacent aquifers. Total estimated outflows were about 99-percent evapotranspiration and less than 1-percent outflow to adjacent aquifers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205097","issn":"2328-0328","usgsCitation":"Eldridge, W.G., and Anderson, T.M., 2020, Hydrogeology and groundwater flow in alluvial deposits, north Summerset, South Dakota: U.S. Geological Survey Scientific Investigations Report 2020–5097, 31 p., https://doi.org/10.3133/sir20205097.","productDescription":"Report: vii, 31 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-116994","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":379700,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5097/coverthb.jpg"},{"id":379703,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","description":"USGS data release","linkHelpText":"USGS Water Data for the Nation"},{"id":379702,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TKVMXU","text":"USGS data release","description":"USGS data release","linkHelpText":"Soil-Water Balance model for alluvial deposits in Summerset, South Dakota"},{"id":379701,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5097/sir20205097.pdf","text":"Report","size":"5.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5097"}],"country":"United States","state":"South Dakota","city":"Sommerset","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.37310791015625,\n 44.15856343854312\n ],\n [\n -103.28109741210938,\n 44.15856343854312\n ],\n [\n -103.28109741210938,\n 44.203866109361435\n ],\n [\n -103.37310791015625,\n 44.203866109361435\n ],\n [\n -103.37310791015625,\n 44.15856343854312\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702
Excessive nutrient inputs from tributary streams and rivers contribute to harmful algal blooms and coastal ecosystem degradation worldwide. However, the role that small tributaries play in coastal nutrient dynamics remains unknown because most monitoring and regulatory efforts focus only on the largest tributaries. We combined a 6-d sampling effort with discharge modeling to characterize nutrient inputs from nearly all watersheds draining to the world’s fifth largest lake. We found that streams are particularly likely to promote eutrophication in coastal ecosystems because they deliver water with higher concentrations of nutrients that are readily available to algae. Thus, our findings indicate that efforts to control nutrient loading could be enhanced by looking beyond the largest tributaries to include smaller streams.
The Midcontinent Rift System (MRS) is a 1.1 Ga sequence of voluminous basaltic eruptions and multiple intrusions followed by widespread sedimentation that extends across the Midcontinent and northern Great Lakes region of North America. Previous workers have commonly used seismic-reflection data (Great Lakes International Multidisciplinary Program on Crustal Evolution [GLIMPCE] line A) to demonstrate that the northern rift margin in central Lake Superior developed as a normal growth fault that was structurally inverted to a reverse fault during a compressional event after rifting had ended. A prominent, curvilinear aeromagnetic anomaly that extends from Isle Royale, Michigan, to Superior Shoal in central Lake Superior, Ontario (the IR-SS anomaly), is commonly presented as a manifestation of this reverse fault. We have integrated multidisciplinary geophysical analyses (seismic-reflection, seismic-refraction, aeromagnetic, and gravity), physical-property information (density, magnetic susceptibility and remanence, and compressional-wave velocity), and geologic concepts to develop an alternate interpretation of the rift margin along GLIMPCE line A, where it intersects the IR-SS anomaly. Our new model indicates that a normal fault is the dominant structure at the northern rift margin along line A, contrary to the original rift-margin paradigm, which asserts that compressional structures are the dominant features preserved today. Integral to this alternate model is a newly interpreted, prerift sedimentary basin intruded by sills in northern Lake Superior. Our alternate model of the northern rift margin has implications for interpreting the style, scale, and timing of extension, rift-related intrusion, and compression during development of the MRS.
Lava that erupted during the 2014–2015 Holuhraun eruption in Iceland flowed into a proglacial river system, resulting in aqueous cooling of the lava and an ephemeral hydrothermal system. We carried out a monitoring study of this system from 2015 to 2018 to document the cooling of the lava over this time, using thermocouple measurements and data-logging sensors. The heat loss rate from advection through this hydrothermal system in August 2015 was ~5.5 × 108 W; since eruption, aqueous cooling likely accounted for ~1% of the total heat loss from the lava. This estimate excludes steam losses from fumaroles as well as any groundwater that was not released to the surface, and thus is a lower bound. Near the terminus of the flow, advection of heat by flowing water may have locally accounted for tens of percent of the total cooling of that part of the flow. Our data quantify the importance of water cooling for this lava flow and can be compared with models to better understand lava–water interactions more generally. We also provide detailed methods for simple, low-cost monitoring of similar instances in the future.
As climate warming and precipitation increase at high latitudes, permafrost terrains across the circumpolar north are poised for intensified geomorphic activity and sediment mobilization that are expected to persist for millennia. In previously glaciated permafrost terrain, ice-rich deposits are associated with large stores of reactive mineral substrate. Over geological timescales, chemical weathering moderates atmospheric CO2 levels, raising the prospect that mass wasting driven by terrain consolidation following thaw (thermokarst) may enhance weathering of permafrost sediments and thus climate feedbacks. The nature of these feedbacks depends upon the mineral composition of sediments (weathering sources) and the balance between atmospheric exchange of CO2 vs. fluvial export of carbonate alkalinity (Σ[
Autonomous underwater vehicles (AUVs) and animal telemetry have become important tools for understanding the relationships between aquatic organisms and their environment, but more information is needed to guide the development and use of AUVs as effective animal tracking platforms. A forward-facing acoustic telemetry receiver (VR2Tx 69 kHz; VEMCO, Bedford, Nova Scotia) attached to a novel AUV (gliding robotic fish) was tested in a freshwater lake to (1) compare its detection efficiency (i.e., the probability of detecting an acoustic signal emitted by a tag) of acoustic tags (VEMCO model V8-4H 69 kHz) to stationary receivers and (2) determine if detection efficiency was related to distance between tag and receiver, direction of movement (toward or away from transmitter), depth, or pitch.
Detection efficiency for mobile (robot-mounted) and stationary receivers were similar at ranges less than 300 m, on average across all tests, but detection efficiency for the mobile receiver decreased faster than for stationary receivers at distances greater than 300 m. Detection efficiency was higher when the robot was moving toward the transmitter than when moving away from the transmitter. Detection efficiency decreased with depth (surface to 4 m) when the robot was moving away from the transmitter, but depth had no significant effect on detection efficiency when the robot was moving toward the transmitter. Detection efficiency was higher when the robot was descending (pitched downward) than ascending (pitched upward) when moving toward the transmitter, but pitch had no significant effect when moving away from the transmitter.
Results suggested that much of the observed variation in detection efficiency is related to shielding of the acoustic signal by the robot body depending on the positions and orientation of the hydrophone relative to the transmitter. Results are expected to inform hardware, software, and operational changes to gliding robotic fish that will improve detection efficiency. Regardless, data on the size and shape of detection efficiency curves for gliding robotic fish will be useful for planning future missions and should be relevant to other AUVs for telemetry. With refinements, gliding robotic fish could be a useful platform for active tracking of acoustic tags in certain environments.
","language":"English","publisher":"Springer","doi":"10.1186/s40317-020-00219-7","usgsCitation":"Ennasr, O., Holbrook, C., Hondorp, D.W., Krueger, C., Coleman, D., Solanki, P., Thon, J., and Tan, X., 2020, Characterization of acoustic detection efficiency using a gliding robotic fish as a mobile receiver platform: Animal Biotelemetry, v. 8, no. 32, 13 p., https://doi.org/10.1186/s40317-020-00219-7.","productDescription":"13 p.","ipdsId":"IP-122951","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":454977,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-020-00219-7","text":"Publisher Index Page"},{"id":436745,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S75TSB","text":"USGS data release","linkHelpText":"Acoustic detection performance of gliding robotic fish in Higgins Lake, Michigan, USA, 2016-2018"},{"id":380901,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"32","noUsgsAuthors":false,"publicationDate":"2020-10-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Ennasr, Osama 0000-0002-8353-6446","orcid":"https://orcid.org/0000-0002-8353-6446","contributorId":245318,"corporation":false,"usgs":false,"family":"Ennasr","given":"Osama","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":805904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":805905,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Krueger, Charles C.","contributorId":67821,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles C.","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":805906,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Coleman, Demetris","contributorId":245319,"corporation":false,"usgs":false,"family":"Coleman","given":"Demetris","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805907,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Solanki, Pratap","contributorId":245320,"corporation":false,"usgs":false,"family":"Solanki","given":"Pratap","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805908,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Thon, John","contributorId":245321,"corporation":false,"usgs":false,"family":"Thon","given":"John","email":"","affiliations":[{"id":49149,"text":"Department of Electrical and Computer Engineering, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":805909,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tan, Xiaobo 0000-0002-5542-6266","orcid":"https://orcid.org/0000-0002-5542-6266","contributorId":214765,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":805910,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70216563,"text":"70216563 - 2020 - A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species","interactions":[],"lastModifiedDate":"2020-11-25T15:25:24.210332","indexId":"70216563","displayToPublicDate":"2020-10-23T09:22:37","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1636,"text":"Fire Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species","docAbstract":"Predictive models of post-fire tree and stem mortality are vital for management planning and understanding fire effects. Post-fire tree and stem mortality have been traditionally modeled as a simple empirical function of tree defenses (e.g., bark thickness) and fire injury (e.g., crown scorch). We used the Fire and Tree Mortality database (FTM)—which includes observations of tree mortality in obligate seeders and stem mortality in basal resprouting species from across the USA—to evaluate the accuracy of post-fire mortality models used in the First Order Fire Effects Model (FOFEM) software system. The basic model in FOFEM, the Ryan and Amman (R-A) model, uses bark thickness and percentage of crown volume scorched to predict post-fire mortality and can be applied to any species for which bark thickness can be calculated (184 species-level coefficients are included in the program). FOFEM (v6.7) also includes 38 species-specific tree mortality models (26 for gymnosperms, 12 for angiosperms), with unique predictors and coefficients. We assessed accuracy of the R-A model for 44 tree species and accuracy of 24 species-specific models for 13 species, using data from 93 438 tree-level observations and 351 fires that occurred from 1981 to 2016.
For each model, we calculated performance statistics and provided an assessment of the representativeness of the evaluation data. We identified probability thresholds for which the model performed best, and the best thresholds with either ≥80% sensitivity or specificity. Of the 68 models evaluated, 43 had Area Under the Receiver Operating Characteristic Curve (AUC) values ≥0.80, indicating excellent performance, and 14 had AUCs <0.7, indicating poor performance. The R-A model often over-predicted mortality for angiosperms; 5 of 11 angiosperms had AUCs <0.7. For conifers, R-A over-predicted mortality for thin-barked species and for small diameter trees. The species-specific models had significantly higher AUCs than the R-A models for 10 of the 22 models, and five additional species-specific models had more balanced errors than R-A models, even though their AUCs were not significantly different or were significantly lower.
Approximately 75% of models tested had acceptable, excellent, or outstanding predictive ability. The models that performed poorly were primarily models predicting stem mortality of angiosperms or tree mortality of thin-barked conifers. This suggests that different approaches—such as different model forms, better estimates of bark thickness, and additional predictors—may be warranted for these taxa. Future data collection and research should target the geographical and taxonomic data gaps and poorly performing models identified in this study. Our evaluation of post-fire tree mortality models is the most comprehensive effort to date and allows users to have a clear understanding of the expected accuracy in predicting tree death from fire for 44 species.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s42408-020-00082-0","usgsCitation":"Cansler, C., Hood, S.M., van Mantgem, P., and Varner, J.M., 2020, A large database supports the use of simple models of post-fire tree mortality for thick-barked conifers, with less support for other species: Fire Ecology, v. 16, 25, 37 p., https://doi.org/10.1186/s42408-020-00082-0.","productDescription":"25, 37 p.","ipdsId":"IP-115072","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":454980,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s42408-020-00082-0","text":"Publisher Index Page"},{"id":380782,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"16","noUsgsAuthors":false,"publicationDate":"2020-10-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Cansler, C. Alina","contributorId":245203,"corporation":false,"usgs":false,"family":"Cansler","given":"C. Alina","affiliations":[{"id":49115,"text":"USDA Forest Service, Rocky Mountain Research Station, Fire, Fuel, and Smoke Science Program, 5775 US Highway 10 W, Missoula, Montana, 59808, USA","active":true,"usgs":false}],"preferred":false,"id":805617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hood, Sharon M.","contributorId":221183,"corporation":false,"usgs":false,"family":"Hood","given":"Sharon","email":"","middleInitial":"M.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":805618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":805619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Varner, J. Morgan 0000-0003-3781-5839","orcid":"https://orcid.org/0000-0003-3781-5839","contributorId":244802,"corporation":false,"usgs":false,"family":"Varner","given":"J.","email":"","middleInitial":"Morgan","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":805620,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215983,"text":"70215983 - 2020 - Double exposure and dynamic vulnerability: Assessing economic well-being, ecological change and the development of the oil and gas industry in coastal Louisiana","interactions":[],"lastModifiedDate":"2020-11-02T14:02:27.322344","indexId":"70215983","displayToPublicDate":"2020-10-23T07:58:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3385,"text":"Shore & Beach","printIssn":"0037-4237","active":true,"publicationSubtype":{"id":10}},"title":"Double exposure and dynamic vulnerability: Assessing economic well-being, ecological change and the development of the oil and gas industry in coastal Louisiana","docAbstract":"The oil and gas industry has been a powerful driver of economic change in coastal Louisiana for the latter half of the 20th century and into the 21st. Yet, the overall impact of the industry on the economic well-being of host communities is varied, both spatially and temporally. While the majority of Louisiana’s oil and gas production now occurs offshore, processing the extracted product is an energy-intensive undertaking requiring an expansive network of land-based infrastructure. Despite the positive economic aspects of this development, there are also potential negatives posed to coastal ecosystems and to communities located adjacent to oil and gas infrastructure. This research utilizes a double exposure framework to explore the relationship between oil and gas infrastructure development, fish and shellfish habitat, and economic well-being in Louisiana’s coastal zone from 1950 to 2010. The approach followed four main steps: (1) Developing a hazardousness of place model to identify areas of magnified risk due to the combined hazards of multiple potential exposure sites related to the extraction and processing of crude oil and natural gas; (2) developing a model of ecological functioning to measure the ability of aquatic habitat to support key fish and shellfish species; (3) utilizing an integrated community economic well-being index to assess change on a decadal timescale; and (4) analyzing selected oil-dependent communities to illustrate how change processes occurring in different energy sectors result in differential outcomes. The results suggest that, for many communities, the dependence on the oil and gas industry has increased economic well-being but also increased sensitivity to natural and human-induced changes, including fluctuating economic conditions, environmental stress, coastal habitat destruction, and increasing social and economic pressures.","language":"English","publisher":"American Shore and Beach Preservation Association (ASBPA)","doi":"10.34237/1008819","usgsCitation":"Hemmerling, S., Carruthers, T., Hijuelos, A., and Bienn, H.C., 2020, Double exposure and dynamic vulnerability: Assessing economic well-being, ecological change and the development of the oil and gas industry in coastal Louisiana: Shore & Beach, v. 88, no. 1, p. 72-82, https://doi.org/10.34237/1008819.","productDescription":"11 p.","startPage":"72","endPage":"82","ipdsId":"IP-112627","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":380018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -93.779296875,\n 28.43971381702788\n ],\n [\n -89.05517578125,\n 28.43971381702788\n ],\n [\n -89.05517578125,\n 30.543338954230222\n ],\n [\n -93.779296875,\n 30.543338954230222\n ],\n [\n -93.779296875,\n 28.43971381702788\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"88","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-03-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hemmerling, Scott","contributorId":221274,"corporation":false,"usgs":false,"family":"Hemmerling","given":"Scott","affiliations":[],"preferred":false,"id":803667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carruthers, Tim J. B.","contributorId":140566,"corporation":false,"usgs":false,"family":"Carruthers","given":"Tim J. B.","affiliations":[],"preferred":false,"id":803668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hijuelos, Ann 0000-0003-0922-6754","orcid":"https://orcid.org/0000-0003-0922-6754","contributorId":201525,"corporation":false,"usgs":true,"family":"Hijuelos","given":"Ann","email":"","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":803669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bienn, Harris C.","contributorId":244280,"corporation":false,"usgs":false,"family":"Bienn","given":"Harris","email":"","middleInitial":"C.","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":803670,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219095,"text":"70219095 - 2020 - Diverse cataclysmic floods from Pleistocene glacial Lake Missoula","interactions":[],"lastModifiedDate":"2021-04-27T11:52:46.725403","indexId":"70219095","displayToPublicDate":"2020-10-23T07:32:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7779,"text":"GSA Special Volume on Pleistocene megafloods","active":true,"publicationSubtype":{"id":10}},"title":"Diverse cataclysmic floods from Pleistocene glacial Lake Missoula","docAbstract":"In late Wisconsin time, the Purcell Trench lobe of the Cordilleran ice sheet dammed the Clark Fork of the Columbia River in western Montana, creating glacial Lake Missoula. During part of this epoch, the Okanogan lobe also dammed the Columbia River downstream, creating glacial Lake Columbia in northeast Washington. Repeated failure of the Purcell Trench ice dam released glacial Lake Missoula, causing dozens of catastrophic floods in eastern Washington that can be distinguished by the geologic record they left behind. These floods removed tens of meters of pale loess from dark basalt substrate, forming scars along flowpaths visible from space.
Different positions of the Okanogan lobe are required for modeled Missoula floods to inundate the diverse channels that show field evidence for flooding, as shown by accurate dam-break flood modeling using a roughly 185 m digital terrain model of existing topography (with control points dynamically varied using automatic mesh refinement). The maximum extent of the Okanogan lobe, which blocked inundation of the upper Grand Coulee and the Columbia River valley, is required to flood all channels in the Telford scablands and to produce highest flood stages in Pasco Basin. Alternatively, the Columbia River valley must have been open and the upper Grand Coulee blocked to nearly match evidence for high water on Pangborn bar near Wenatchee, Washington, and to flood Quincy Basin from the west. Finally, if the Columbia River valley and upper Grand Coulee were both open, Quincy Basin would have flooded from the northeast.
In all these scenarios, the discrepancy between modeled flood stages and field evidence for maximum flood stages increases in all channels downstream, from Spokane to Umatilla Basin. The pattern of discrepancies indicates that bulking of floods by loess increased flow volume across the scablands, but this alone does not explain low modeled flow stages along the Columbia River valley near Wenatchee. This latter discrepancy between modeled flood stages and field data requires either additional bulking of flow by sediment along the Columbia reach downstream of glacial Lake Columbia, or coincident dam failures of glacial Lake Columbia and glacial Lake Missoula.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2021.2548(17)","usgsCitation":"Denlinger, R.P., George, D.L., Cannon, C.M., O'Connor, J., and Waitt, R.B., 2020, Diverse cataclysmic floods from Pleistocene glacial Lake Missoula: GSA Special Volume on Pleistocene megafloods, v. 548, 18 p., https://doi.org/10.1130/2021.2548(17).","productDescription":"18 p.","ipdsId":"IP-101636","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":384572,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Washington","otherGeospatial":"Lake Missoula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.92675781249999,\n 46.08847179577592\n ],\n [\n -113.3349609375,\n 46.08847179577592\n ],\n [\n -113.3349609375,\n 48.22467264956519\n ],\n [\n -119.92675781249999,\n 48.22467264956519\n ],\n [\n -119.92675781249999,\n 46.08847179577592\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"548","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Denlinger, Roger P. 0000-0003-0930-0635 roger@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-0635","contributorId":2679,"corporation":false,"usgs":true,"family":"Denlinger","given":"Roger","email":"roger@usgs.gov","middleInitial":"P.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":812746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":812747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cannon, Charles M. 0000-0003-4136-2350 ccannon@usgs.gov","orcid":"https://orcid.org/0000-0003-4136-2350","contributorId":247680,"corporation":false,"usgs":true,"family":"Cannon","given":"Charles","email":"ccannon@usgs.gov","middleInitial":"M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":812748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":812749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Waitt, Richard B. 0000-0002-6392-5604 waitt@usgs.gov","orcid":"https://orcid.org/0000-0002-6392-5604","contributorId":2343,"corporation":false,"usgs":true,"family":"Waitt","given":"Richard","email":"waitt@usgs.gov","middleInitial":"B.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":812750,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215976,"text":"70215976 - 2020 - Geomorphic and sedimentary effects of modern climate change: Current and anticipated future conditions in the western United States","interactions":[],"lastModifiedDate":"2020-12-14T16:49:17.200792","indexId":"70215976","displayToPublicDate":"2020-10-23T07:02:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3283,"text":"Reviews of Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Geomorphic and sedimentary effects of modern climate change: Current and anticipated future conditions in the western United States","docAbstract":"Hydroclimatic changes associated with global warming over the past 50 years have been documented widely, but physical landscape responses are poorly understood thus far. Detecting sedimentary and geomorphic signals of modern climate change presents challenges owing to short record lengths, difficulty resolving signals in stochastic natural systems, influences of land use and tectonic activity, long‐lasting effects of individual extreme events, and variable connectivity in sediment‐routing systems. We review existing literature to investigate the nature and extent of sedimentary and geomorphic responses to modern climate change, focusing on the western United States, a region with generally high relief and high sediment yield likely to be sensitive to climatic forcing. Based on fundamental geomorphic theory and empirical evidence from other regions, we anticipate climate‐driven changes to slope stability, watershed sediment yields, fluvial morphology, and aeolian sediment mobilization in the western U.S. We find evidence for recent climate‐driven changes to slope stability and increased aeolian dune and dust activity, whereas changes in sediment yields and fluvial morphology have been linked more commonly to non‐climatic drivers thus far. Detecting effects of climate change will require better understanding how landscape response scales with disturbance, how lag times and hysteresis operate within sedimentary systems, and how to distinguish the relative influence and feedbacks of superimposed disturbances. The ability to constrain geomorphic and sedimentary response to rapidly progressing climate change has widespread implications for human health and safety, infrastructure, water security, economics, and ecosystem resilience.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019RG000692","usgsCitation":"East, A.E., and Sankey, J.B., 2020, Geomorphic and sedimentary effects of modern climate change: Current and anticipated future conditions in the western United States: Reviews of Geophysics, v. 58, no. 4, e2019RG000692, 59 p., https://doi.org/10.1029/2019RG000692.","productDescription":"e2019RG000692, 59 p.","ipdsId":"IP-115204","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":454985,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019rg000692","text":"Publisher Index Page"},{"id":380009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nevada, New Mexico, Oregon, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.5078125,\n 31.052933985705163\n ],\n [\n -103.6669921875,\n 31.052933985705163\n ],\n [\n -103.6669921875,\n 48.951366470947725\n ],\n [\n -125.5078125,\n 48.951366470947725\n ],\n [\n -125.5078125,\n 31.052933985705163\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"58","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-03","publicationStatus":"PW","contributors":{"authors":[{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":803644,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sankey, Joel B. 0000-0003-3150-4992 jsankey@usgs.gov","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":3935,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel","email":"jsankey@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":803645,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215132,"text":"70215132 - 2020 - Spectral wave-driven bedload transport across a coral reef flat/lagoon complex","interactions":[],"lastModifiedDate":"2020-10-29T15:04:55.965749","indexId":"70215132","displayToPublicDate":"2020-10-22T10:04:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Spectral wave-driven bedload transport across a coral reef flat/lagoon complex","docAbstract":"Coral reefs are an important source of sediment for reef-lined coasts by helping to maintain beaches while also providing protection in the form of wave energy dissipation. Understanding the mechanisms by which sediment is delivered to the coast as well as better constraining the total volumes generated are critical for projecting future coastal change. A month-long hydrodynamics and sediment transport study on a fringing reef/lagoon complex in Western Australia indicates that lower frequency constituents of wave energy are important to the total bedload transport of sediment across the reef flat and lagoon to the shoreline. The reef flat and the lagoon are characterized by distinctly different transport regimes, resulting in an offset in the timing of bedform migration between the two. Short-term storage of sediment is noted on the reef flat, which is subsequently washed out into the lagoon when offshore wave heights increase and strong currents due to wave breaking at the reef crest develop. This sudden influx of sediment is a significant control on bedform migration rates in the lagoon. Infragravity wave energy on the reef flat and lagoon make an important contribution to the migration of bedforms and resultant bedload transport. Given the complexity of the hydrodynamics of fringing reefs, the transfer of energy to lower frequency bands, as well as accurate estimates of sources and sinks of sediment, must but considered in order to correctly model the transport of sediment from the reef to the coast.
Quantifying the distribution of animals and identifying underlying characteristics that define suitable habitat are essential for effective conservation of free-ranging species. Prioritizing areas for conservation is important in managing a geographic extent that has a high level of disturbance and limited conservation resources. We examined the potential use of a species distribution model ensemble for multi-species conservation in marine habitats. Using satellite telemetry locations during foraging as input data, and ensemble ecological niche models, we predicted foraging areas for 2 nesting marine turtle species within the Gulf of Mexico (GoM): Kemp’s ridley Lepidochelys kempii (n = 63) and loggerhead Caretta caretta (n = 63). We considered 7 geophysical, biological, and climatic variables and compared contributing factors for each species’ foraging habitat selection. For both species, predicted suitable foraging habitats encompassed large areas along the GoM coast, but only intersected with each other in relatively small areas. Highly parameterized models resulted in overall greater fits, suggesting that multiple factors influence habitat selection by these species. Model validation results were mixed: cross-validation resulted in high prediction accuracy for both species, but an evaluation against independent data resulted in a low omission rate (5%) for Kemp’s ridleys and a high omission rate (72%) for loggerheads. The relatively small intersection of model-predicted foraging areas for these 2 species within the study area may indicate possible niche differentiations. The high omission rate for loggerheads indicates our samples likely underrepresent the population and illustrates the challenges in predicting suitable foraging extents for species that make dynamic movements and have greater individual variability.
","language":"English","publisher":"Inter Research","doi":"10.3354/esr01059","usgsCitation":"Fujisaki, I., Hart, K., Bucklin, D.N., Iverson, A., Rubio, C., Lamont, M.M., Miron, R.D., Burchfield, P., Pena, J., and Shaver, D.J., 2020, Predicting multi-species foraging hotspots for marine turtles in the Gulf of Mexico: Endangered Species Research, v. 43, p. 253-266, https://doi.org/10.3354/esr01059.","productDescription":"14 p.","startPage":"253","endPage":"266","ipdsId":"IP-120330","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":454990,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01059","text":"Publisher Index Page"},{"id":380022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Mexico","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.701171875,\n 21.779905342529645\n ],\n [\n -96.0205078125,\n 18.22935133838668\n ],\n [\n -93.2958984375,\n 17.518344187852218\n ],\n [\n -91.0546875,\n 18.104087015773956\n ],\n [\n -80.5517578125,\n 24.966140159912975\n ],\n [\n -82.6171875,\n 30.977609093348686\n ],\n [\n -97.822265625,\n 30.486550842588485\n ],\n [\n -98.701171875,\n 21.779905342529645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fujisaki, Ikuko","contributorId":38359,"corporation":false,"usgs":false,"family":"Fujisaki","given":"Ikuko","affiliations":[],"preferred":false,"id":803648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":206816,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":803649,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bucklin, David N.","contributorId":175273,"corporation":false,"usgs":false,"family":"Bucklin","given":"David","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":803650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iverson, Autumn R. 0000-0002-8353-6745","orcid":"https://orcid.org/0000-0002-8353-6745","contributorId":173555,"corporation":false,"usgs":false,"family":"Iverson","given":"Autumn R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":803651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rubio, Cynthia","contributorId":244274,"corporation":false,"usgs":false,"family":"Rubio","given":"Cynthia","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":803652,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lamont, Margaret M. 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":218323,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","email":"","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":803653,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miron, Raul de Jesus G.D.","contributorId":244275,"corporation":false,"usgs":false,"family":"Miron","given":"Raul","email":"","middleInitial":"de Jesus G.D.","affiliations":[{"id":48880,"text":"Acuario de Veracruz A.C., Veracruz, Veracruz Mexico","active":true,"usgs":false}],"preferred":false,"id":803654,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Burchfield, Patrick M.","contributorId":244276,"corporation":false,"usgs":false,"family":"Burchfield","given":"Patrick M.","affiliations":[{"id":48881,"text":"Gladys Porter Zoo","active":true,"usgs":false}],"preferred":false,"id":803655,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pena, Jaime","contributorId":168392,"corporation":false,"usgs":false,"family":"Pena","given":"Jaime","email":"","affiliations":[],"preferred":false,"id":803656,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shaver, Donna J.","contributorId":191186,"corporation":false,"usgs":false,"family":"Shaver","given":"Donna","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":803657,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70224302,"text":"70224302 - 2020 - Spatial fingerprint of younger dryas cooling and warming in eastern North America","interactions":[],"lastModifiedDate":"2021-09-21T13:03:33.362473","indexId":"70224302","displayToPublicDate":"2020-10-22T08:00:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Spatial fingerprint of younger dryas cooling and warming in eastern North America","docAbstract":"The Younger Dryas (YD, 12.9–11.7 ka) is the most recent, near-global interval of abrupt climate change with rates similar to modern global warming. Understanding the causes and biodiversity effects of YD climate changes requires determining the spatial fingerprints of past temperature changes. Here we build pollen-based and branched glycerol dialkyl glycerol tetraether-based temperature reconstructions in eastern North America (ENA) to better understand deglacial temperature evolution. YD cooling was pronounced in the northeastern United States and muted in the north central United States. Florida sites warmed during the YD, while other southeastern sites maintained a relatively stable climate. This fingerprint is consistent with an intensified subtropical high during the YD and demonstrates that interhemispheric responses were more complex spatially in ENA than predicted by the bipolar seesaw model. Reduced-amplitude or antiphased millennial-scale temperature variability in the southeastern United States may support regional hotspots of biodiversity and endemism.
Chlorinated volatile organic compounds (CVOCs) have migrated to groundwater beneath a former 9-acre landfill at Operable Unit 1 (OU-1) on Naval Base Kitsap, which was active from the 1930s through 1973 on the Keyport Peninsula, in Kitsap County, Washington. Biodegradation of CVOCs at OU-1 limits the mass of dissolved-phase CVOCs in groundwater that discharges to surface water, but contaminant concentrations up to 630 milligrams per liter persist in localized areas, likely from the dissolution of residual, non-aqueous phase liquids. Variable-density groundwater-flow and contaminant-transport models were developed using the SEAWAT-Version 4 computer program to simulate the direction and rate of groundwater flow in a 5.9 square-mile (mi2) - area surrounding the Keyport Peninsula, to estimate the CVOC mass in groundwater and the rate of mass loading, and to assess possible remedial activities at OU-1.
The study area is underlain by Quaternary deposits consisting of alternating glacial and interglacial sediments ranging from 500 to 1,500 feet (ft) thick. A hydrogeologic model delineated a sequence of 10 units including a relatively thin package (less than 100 ft) of recent sediments (Vashon Stade and younger) beneath the Keyport Peninsula that are underlain by the much thicker (more than 300 ft) Clover Park Aquitard, which overlies a confined, sea-level aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205066","collaboration":"Prepared in cooperation with the Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Yager, R.M., Welch, W.B., Headman, A., and Dinicola, R.S., 2020, Variable-density groundwater flow and contaminant transport, Operable Unit 1, Naval Base Kitsap, Keyport, Washington: U.S. Geological Survey Scientific Investigations Report 2020–5066, 58 p., https://doi.org/10.3133/sir20205066.","productDescription":"x, 62 p.","onlineOnly":"Y","ipdsId":"IP-112628","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":379666,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95WQ7TM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil water balance (SWB) model of Keyport, Washington"},{"id":379617,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5066/sir20205066.pdf","text":"Report","size":"10.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5066"},{"id":379667,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YNPPNL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005, MODFLOW-NWT, and SEAWAT V.4 models used to simulate variable-density groundwater flow and contaminant transport at Naval Base Kitsap, Keyport, Washington"},{"id":379616,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5066/coverthb2.jpg"}],"country":"United States","state":"Washington","city":"Keyport","otherGeospatial":"Naval Base Kitsap","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.65,\n 47.6666\n ],\n [\n -122.60833,\n 47.6666\n ],\n [\n -122.60833,\n 47.71666\n ],\n [\n -122.65,\n 47.71666\n ],\n [\n -122.65,\n 47.6666\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Declining water levels and reduced natural discharge at springs, seeps, and phreatophyte areas primarily are the result of decades of groundwater development in the Death Valley regional flow system, in Nevada and California. A calibrated groundwater-flow model was used to simulate potential future effects of groundwater pumping on water levels and natural groundwater discharge in the study area. Effects of climate change on future groundwater pumping were not considered and were beyond the scope of the study. Four groundwater-pumping scenarios were developed by stakeholders to predict and compare (1) the extent of regional water-level declines; (2) drawdown at Devils Hole; and (3) reductions in natural discharge at select discharge areas, including the Amargosa Wild and Scenic River, the Ash Meadows discharge area, the Furnace Creek area, and Stump Spring. Scenarios were simulated from 1913 to 2120, with historical pumping occurring from 1913 to 2010, historical 2010 pumping rates projected from 2010 to 2020, and scenario pumping beginning in 2020. Pumping scenarios included a base case and scenarios A, B, and C. The base case projected 2010 pumping rates from 2010 to 2120, and scenarios A, B, and C projected base case pumping plus additional pumping at various locations from 2020 to 2120. By 2020, historical (1913–2020) pumping resulted in the propagation of simulated drawdown of 1 foot (ft) or more westward from Pahrump Valley to areas north of Shoshone in the Pahrump to Death Valley South (PDVS) groundwater basin and the merging of simulated 1-ft drawdown contours between the Alkali Flat–Furnace Creek Ranch (AFFCR) and Ash Meadows groundwater basins. In the base case scenario, extent and magnitude of simulated drawdown continued to increase in the Ash Meadows and AFFCR groundwater basins from 2020 to 2120. In the base case, the magnitude of simulated drawdown continued to increase in western Pahrump Valley from 2020 to 2120, whereas simulated water levels rose in eastern Pahrump Valley from 2020 to 2070 and then stabilized from 2070 to 2120. Scenarios A and B primarily affected the PDVS and AFFCR groundwater basins by increasing the magnitude of drawdown in 2120, compared to the base case. In scenario C, drawdown propagated throughout a high-transmissivity part of the carbonate aquifer known as the megachannel, greatly affecting water levels in the Ash Meadows discharge area. Scenario C resulted in an additional 10–100 ft of drawdown (compared to the base case) throughout the southeastern part of the Ash Meadows groundwater basin by 2120. Simulated drawdowns in Devils Hole in 2120 were 3.2, 3.4, 3.8, and 25.4 ft for the base case and scenarios A, B, and C, respectively. The federally mandated minimum water level for Devils Hole is 2.7 ft below a reference point. In 2020, the simulated water level in Devils Hole was above the minimum water level, at 1.7 ft below the reference. Simulated water levels in Devils Hole fell below the federally mandated water level by 2078, 2073, 2058, and 2025 for the base case and scenarios A, B, and C, respectively, assuming a hypothetical recharge scenario of constant natural recharge. Simulated reductions in predevelopment (natural) discharge at select discharge areas ranged from 3 to 38 percent by 2120 for all scenarios. Amargosa Wild and Scenic River was the least affected discharge area with simulated capture rates ranging from 3 to 4 percent of predevelopment discharge by 2120. Ash Meadows discharge area was greatly affected by groundwater pumping in scenario C with a simulated capture rate of 38 percent, compared to simulated capture rates of 8, 8, and 9 percent for the base case, scenario A, and scenario B, respectively, in 2120. Simulated capture rates in the Furnace Creek area ranged from 10 to 11 percent for all scenarios in 2120. Simulated capture rates at Stump Spring ranged from 32 to 36 percent for all scenarios in 2120.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205103","collaboration":"Prepared in cooperation with the Bureau of Land Management; National Park Service; Nevada Division of Wildlife; Nye County, Nevada; and U.S. Fish and Wildlife Service","usgsCitation":"Nelson, N.C., and Jackson, T.R., 2020, Simulated effects of pumping in the Death Valley Regional Groundwater Flow System, Nevada and California—Selected management scenarios projected to 2120: U.S. Geological Survey Scientific Investigations Report 2020–5103, 30 p., https://doi.org/10.3133/sir20205103.","productDescription":"Report: vii, 30 p.; Data Releases","onlineOnly":"Y","ipdsId":"IP-112177","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":379438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5103/coverthb.jpg"},{"id":379439,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5103/sir20205103.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5103"},{"id":379440,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OBUPXU","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 models used to simulate effects of pumping in the Death Valley Regional Groundwater Flow System, Nevada and California—Selected management scenarios projected to 2120"},{"id":379476,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75H7FH3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Update to the groundwater withdrawals database for the Death Valley regional groundwater flow system, Nevada and California, 1913 -2010"},{"id":379477,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HIYVG2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 model and supplementary data used to characterize groundwater flow and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley Regional Groundwater Flow System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.3779296875,\n 33.62376800118811\n ],\n [\n -114.08203125,\n 33.62376800118811\n ],\n [\n -114.08203125,\n 38.62545397209084\n ],\n [\n -117.3779296875,\n 38.62545397209084\n ],\n [\n -117.3779296875,\n 33.62376800118811\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
Seagrass communities are a vital component of estuarine ecosystems, but are threatened by projected sea level rise (SLR) and temperature increases with climate change. To understand these potential effects, we developed a spatially explicit model that represents seagrass (Zostera marina) habitat and estuary-wide productivity for Barnegat Bay-Little Egg Harbor (BB-LEH) in New Jersey, United States. Our modeling approach included an offline coupling of a numerical seagrass biomass model with the spatially variable environmental conditions from a hydrodynamic model to calculate above and belowground biomass at each grid cell of the hydrodynamic model domain. Once calibrated to represent present day seagrass habitat and estuary-wide annual productivity, we applied combinations of increasing air temperature and sea level following regionally specific climate change projections, enabling analysis of the individual and combined impacts of these variables on seagrass biomass and spatial coverage. Under the SLR scenarios, the current model domain boundaries were maintained, as the land surrounding BB-LEH is unlikely to shift significantly in the future. SLR caused habitat extent to decrease dramatically, pushing seagrass beds toward the coastline with increasing depth, with a 100% loss of habitat by the maximum SLR scenario. The dramatic loss of seagrass habitat under SLR was in part due to the assumption that surrounding land would not be inundated, as the model did not allow for habitat expansion outside the current boundaries of the bay. Temperature increases slightly elevated the rate of summer die-off and decreased habitat area only under the highest temperature increase scenarios. In combined scenarios, the effects of SLR far outweighed the effects of temperature increase. Sensitivity analysis of the model revealed the greatest sensitivity to changes in parameters affecting light limitation and seagrass mortality, but no sensitivity to changes in nutrient limitation constants. The high vulnerability of seagrass in the bay to SLR exceeded that demonstrated for other systems, highlighting the importance of site- and region-specific assessments of estuaries under climate change.
Timing of activity can reveal an organism's efforts to optimize foraging either by minimizing energy loss through passive movement or by maximizing energetic gain through foraging. Here, we assess whether signals of either of these strategies are detectable in the timing of activity of daily, local movements by birds. We compare the similarities of timing of movement activity among species using six temporal variables: start of activity relative to sunrise, end of activity relative to sunset, relative speed at midday, number of movement bouts, bout duration, and proportion of active daytime hours. We test for the influence of flight mode and foraging habitat on the timing of movement activity across avian guilds. We used 64570 days of GPS movement data collected between 2002 and 2019 for local (non‐migratory) movements of 991 birds from 49 species, representing 14 orders. Dissimilarity among daily activity patterns was best explained by flight mode. Terrestrial soaring birds began activity later and stopped activity earlier than pelagic soaring or flapping birds. Broad‐scale foraging habitat explained less of the clustering patterns because of divergent timing of active periods of pelagic surface and diving foragers. Among pelagic birds, surface foragers were active throughout the day while diving foragers matched their active hours more closely to daylight hours. Pelagic surface foragers also had the greatest daily foraging distances, which was consistent with their daytime activity patterns. This study demonstrates that flight mode and foraging habitat influence temporal patterns of daily movement activity of birds.
","language":"English","publisher":"Wiley","doi":"10.1111/jav.02612","usgsCitation":"Mallon, J.M., Tucker, M.A., Beard, A., Bierregaard, R.O., Bildstein, K.L., Böhning-Gaese, K., Brzorad, J.N., Buechley, E., Bustamante, J., Carrapato, C., Castillo-Guerrero, J.A., Clingham, E., Desholm, M., DeSorbo, C.R., Domenech, R., Douglas, H., Duriez, O., Enggist, P., Farwig, N., Fiedler, W., Gagliardo, A., García‐Ripollés, C., Gil Gallus, J.A., Gilmour, M., Harel, R., Harrison, A., Henry, L., Katzner, T., Kays, R., Kleyheeg, E., Limiñana, R., Lopez-Lopez, P., Lucia, G., Maccarone, A., Mallia, E., Mellone, U., Mojica, E., Nathan, R., Newman, S., Oppel, S., Orchan, Y., Prosser, D.J., Riley, H., Rösner, S., Schabo, D.G., Schulz, H., Shaffer, S.A., Shreading, A., Silva, J., Sim, J., Skov, H., Spiegel, O., Stuber, M.J., Takekawa, J.Y., Urios, V., Vidal-Mateo, J., Warner, K., Watts, B.D., Weber, N., Weber, S., Wikelski, M., Zydelis, R., Mueller, T., and Fagan, W., 2020, Diurnal timing of nonmigratory movement by birds: The importance of foraging spatial scales: Journal of Avian Biology, v. 51, no. 12, e02612, 11 p., https://doi.org/10.1111/jav.02612.","productDescription":"e02612, 11 p.","ipdsId":"IP-115942","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":455018,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/jav.02612","text":"External Repository"},{"id":380648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Mallon, Julie M.","contributorId":150853,"corporation":false,"usgs":false,"family":"Mallon","given":"Julie","email":"","middleInitial":"M.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia 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Charlotte","active":true,"usgs":false}],"preferred":false,"id":805213,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bildstein, Keith L.","contributorId":150854,"corporation":false,"usgs":false,"family":"Bildstein","given":"Keith","email":"","middleInitial":"L.","affiliations":[{"id":18119,"text":"Hawk Mountain Sanctuary, Acopian Center for Conservation Learning","active":true,"usgs":false}],"preferred":false,"id":805214,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Böhning-Gaese, Katrin","contributorId":174361,"corporation":false,"usgs":false,"family":"Böhning-Gaese","given":"Katrin","affiliations":[{"id":27439,"text":"Senckenberg Biodiversity and Climate Research Centre","active":true,"usgs":false}],"preferred":false,"id":805312,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brzorad, John 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Sam","contributorId":245120,"corporation":false,"usgs":false,"family":"Weber","given":"Sam","email":"","affiliations":[],"preferred":false,"id":805366,"contributorType":{"id":1,"text":"Authors"},"rank":60},{"text":"Wikelski, Martin","contributorId":76451,"corporation":false,"usgs":true,"family":"Wikelski","given":"Martin","affiliations":[],"preferred":false,"id":805367,"contributorType":{"id":1,"text":"Authors"},"rank":61},{"text":"Zydelis, Ramunas","contributorId":203738,"corporation":false,"usgs":false,"family":"Zydelis","given":"Ramunas","email":"","affiliations":[{"id":35135,"text":"DHI, Hørsholm, Denmark","active":true,"usgs":false}],"preferred":false,"id":805368,"contributorType":{"id":1,"text":"Authors"},"rank":62},{"text":"Mueller, Thomas","contributorId":91393,"corporation":false,"usgs":true,"family":"Mueller","given":"Thomas","affiliations":[],"preferred":false,"id":805369,"contributorType":{"id":1,"text":"Authors"},"rank":63},{"text":"Fagan, William F.","contributorId":108239,"corporation":false,"usgs":true,"family":"Fagan","given":"William F.","affiliations":[],"preferred":false,"id":805370,"contributorType":{"id":1,"text":"Authors"},"rank":64}]}} ,{"id":70215554,"text":"70215554 - 2020 - Modeling three-dimensional flow over spur-and-groove morphology","interactions":[],"lastModifiedDate":"2020-11-30T16:06:18.953016","indexId":"70215554","displayToPublicDate":"2020-10-19T08:24:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1338,"text":"Coral Reefs","active":true,"publicationSubtype":{"id":10}},"title":"Modeling three-dimensional flow over spur-and-groove morphology","docAbstract":"Spur-and-groove (SAG) morphology characterizes the fore reef of many coral reefs worldwide. Although the existence and geometrical properties of SAG have been well documented, an understanding of the hydrodynamics over them is limited. Here, the three-dimensional flow patterns over SAG formations, and a sensitivity of those patterns to waves, currents, and SAG geometry were characterized using the physics-based Delft3D-FLOW and SWAN models. Shore-normal shoaling waves over SAG formations were shown to drive two circulation cells: a cell on the lower fore reef with offshore flow over the spurs and onshore flow over the grooves, except near the seabed where velocities were always onshore, and a cell on the upper fore reef with offshore surface velocities and onshore bottom currents, which result in depth-averaged onshore and offshore flow over the spurs and grooves, respectively. The mechanism driving this flow results from the net of the radiation stress gradients and pressure gradient, which is balanced by the Reynolds stress gradients and bottom friction that differ over the spur and over the groove. Waves were the primary driver of variations in modelled flow over SAG, with the flow strength increasing for increasing wave heights and periods. Spur height, SAG wavelength, and the water depth at peak spur height were the dominant influences on the hydrodynamics, with spur heights directly proportional to the strength of SAG circulation cells. SAG formations with shorter SAG wavelengths only presented one circulation cell on the shallower portion of the reef, as opposed to the two circulation cells for longer SAG wavelengths. SAG formations with peak spur heights occurring in shallower water had stronger circulation than those with peak spur heights occurring in deeper water. These hydrodynamic patterns also likely affect coral and reef development through sediment and nutrient fluxes.
No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Academic Press","usgsCitation":"Royle, A., and Kery, M., 2020, Modeling population dynamics with multinomial count data, chap. 2 of Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS, p. 65-156.","productDescription":"92 p.","startPage":"65","endPage":"156","ipdsId":"IP-101095","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":379643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":379628,"type":{"id":15,"text":"Index Page"},"url":"https://www.elsevier.com/books/applied-hierarchical-modeling-in-ecology-analysis-of-distribution-abundance-and-species-richness-in-r-and-bugs/kery/978-0-12-809585-0"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":802622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kery, Marc","contributorId":168361,"corporation":false,"usgs":false,"family":"Kery","given":"Marc","affiliations":[{"id":12551,"text":"Swiss Ornithological Institute, Sempach, Switzerland","active":true,"usgs":false}],"preferred":false,"id":802707,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216504,"text":"70216504 - 2020 - Injection‐induced earthquakes near Milan, Kansas, controlled by Karstic Networks","interactions":[],"lastModifiedDate":"2020-11-24T13:38:00.985824","indexId":"70216504","displayToPublicDate":"2020-10-19T07:34:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Injection‐induced earthquakes near Milan, Kansas, controlled by Karstic Networks","docAbstract":"Induced earthquakes from waste disposal operations in otherwise tectonically stable regions significantly increases seismic hazard. It remains unclear why injections induce large earthquakes on non‐optimally oriented faults kilometers below the injection horizon, particularly since fluids are not injected under pressure, but rather poured, into the well as observed in the Milan, Kansas area. Here we propose a mechanism for induced earthquakes whereby the karstic lower Arbuckle provides the short‐circuit that establishes a tens of MPa stepwise fluid pressure increase within the basement upon arrival of the hydraulic connection to the free surface and ultimately induce slip on the deeper fault. We investigate this scenario through modeling and mechanical analysis and show that earthquakes near Milan are likely induced by large (and sudden) fluid pressure changes when the karst network links two previously isolated hydrological systems.
Large-domain hydrological models are increasingly needed to support water-resource assessment and management in large river basins. Here, we describe results for the first Brazilian application of the SPAtially Referenced Regression On Watershed attributes (SPARROW) model using a new open-source modeling and interactive decision support system tool (RSPARROW) to quantify the origin, flux, and fate of total nitrogen (TN) in two sub-basins of the Grande River Basin (GRB; 43,000 km2). Land under cultivation for sugar cane, urban land, and point source inputs from wastewater treatment plants was estimated to each contribute approximately 30% of the TN load at the outlet, with pasture land contributing about 10% of the load. Hypothetical assessments of wastewater treatment plant upgrades and the building of new facilities that could treat currently untreated urban runoff suggest that these management actions could potentially reduce loading at the outlet by as much as 20–25%. This study highlights the ability of SPARROW and the RSPARROW mapping tool to assist with the development and evaluation of management actions aimed at reducing nutrient pollution and eutrophication. The freely available RSPARROW modeling tool provides new opportunities to improve understanding of the sources, delivery, and transport of water-quality contaminants in watersheds throughout the world.
","language":"English","publisher":"MDPI","doi":"10.3390/w12102911","usgsCitation":"Miller, M., de Souza, M.L., Alexander, R.B., Gorman Sanisaca, L.E., de Amorim Teixeira, A., and Appling, A.P., 2020, Application of the RSPARROW modeling tool to estimate total nitrogen sources to streams and evaluate source reduction management scenarios in the Grande River Basin, Brazil: Water, v. 12, no. 10, 2911, 20 p., https://doi.org/10.3390/w12102911.","productDescription":"2911, 20 p.","ipdsId":"IP-122604","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":455023,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12102911","text":"Publisher Index Page"},{"id":436752,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FZV0Z0","text":"USGS data release","linkHelpText":"RSPARROW Model Archive Files for the Grande River Basin TN SPARROW Model"},{"id":379649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Grande River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -50.95458984374999,\n -20.324023603422507\n ],\n [\n -49.32861328125,\n -21.46329344189928\n ],\n [\n -48.284912109375,\n -22.451648819126202\n ],\n [\n -46.73583984375,\n -23.29181053244191\n ],\n [\n -45.37353515625,\n -22.61401087437028\n ],\n [\n -44.05517578124999,\n -21.881889807629257\n ],\n [\n -43.5498046875,\n -21.125497636606266\n ],\n [\n -45.736083984375,\n -20.33432561683554\n ],\n [\n -46.35131835937499,\n -20.478481600090554\n ],\n [\n -46.966552734375,\n -20.014645445341355\n ],\n [\n -47.647705078125,\n -19.797717490704724\n ],\n [\n -48.944091796875,\n -19.9526963975442\n ],\n [\n -49.32861328125,\n -19.652934210612436\n ],\n [\n -50.28442382812499,\n -19.425153718960143\n ],\n [\n -50.86669921875,\n -19.756364230752375\n ],\n [\n -50.95458984374999,\n -20.324023603422507\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"de Souza, Marcelo L","contributorId":243598,"corporation":false,"usgs":false,"family":"de Souza","given":"Marcelo","email":"","middleInitial":"L","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":802666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Richard B 0000-0001-9166-0626","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":243599,"corporation":false,"usgs":false,"family":"Alexander","given":"Richard","email":"","middleInitial":"B","affiliations":[{"id":38108,"text":"NA","active":true,"usgs":false}],"preferred":false,"id":802667,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gorman Sanisaca, Lillian E. 0000-0003-1711-3864","orcid":"https://orcid.org/0000-0003-1711-3864","contributorId":210381,"corporation":false,"usgs":true,"family":"Gorman Sanisaca","given":"Lillian","middleInitial":"E.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"de Amorim Teixeira, Alexandre","contributorId":243600,"corporation":false,"usgs":false,"family":"de Amorim Teixeira","given":"Alexandre","email":"","affiliations":[{"id":48748,"text":"Brazilian National Water and Sanitation Agency","active":true,"usgs":false}],"preferred":false,"id":802669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":802670,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}