Cores from living coral colonies were collected from Dry Tortugas National Park, Florida, U.S.A., to obtain skeletal records of past coral growth and allow geochemical reconstruction of environmental variables during the corals’ centuries-long lifespans. The samples were collected as part of the U.S. Geological Survey Coral Reef Ecosystems Studies project (http:/coastal.er.usgs.gov/crest) that provides science to assist resource managers tasked with the stewardship of coral reef resources. Three colonies each of the coral species Orbicella faveolata and Siderastrea siderea were collected in May 2012 using the methods described herein and as approved under National Park Service scientific collecting permit number DRTO-2012-SCI-0001 and are cataloged under accession number DRTO-353. These coral samples can be used to retroactively construct environmental parameters, including sea-surface temperature, by measuring the elemental composition of the coral skeleton. The cores described here, and others (see http://olga.er.usgs.gov/coreviewer/), can be requested, on loan, for scientific study. Photographic images for each coral in its ocean environment, the coral cores as curated and slabbed, and the X-rays of the slabs can be found in an associated U.S. Geological Survey Data Release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161182","usgsCitation":"Weinzierl, M.S., Reich, C.D., Hickey, T.D., Bartlett, L.A., and Kuffner, I.B., 2016, Collection methods and descriptions of coral cores extracted from massive corals in Dry Tortugas National Park, Florida, U.S.A.: U.S. Geological Survey Open-File Report 2016–1182, 8 p., https://doi.org/10.3133/ofr20161182.","productDescription":"Report: iv, 8 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079150","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":331258,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7V69GQ2","text":"USGS data release","description":"USGS data release","linkHelpText":"Coral cores collected in Dry Tortugas National Park, Florida, U.S.A.: Photographs and X-rays"},{"id":331248,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1182/coverthb.jpg"},{"id":331249,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1182/ofr20161182.pdf","text":"Report","size":"6.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1182"}],"country":"United States","state":"Florida","otherGeospatial":"Dry Tortugas National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83,\n 24.5\n ],\n [\n -83,\n 24.8\n ],\n [\n -82.5,\n 24.8\n ],\n [\n -82.5,\n 24.5\n ],\n [\n -83,\n 24.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
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St. Petersburg, FL 33701
http://coastal.er.usgs.gov/
The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy, has maintained a water-level monitoring program at the Idaho National Laboratory (INL) since 1949. The purpose of the program is to systematically measure and report water-level data to assess the eastern Snake River Plain aquifer and long term changes in groundwater recharge, discharge, movement, and storage. Water-level data are commonly used to generate potentiometric maps and used to infer increases and (or) decreases in the regional groundwater system. Well deviation is one component of water-level data that is often overlooked and is the result of the well construction and the well not being plumb. Depending on measured slant angle, where well deviation generally increases linearly with increasing slant angle, well deviation can suggest artificial anomalies in the water table. To remove the effects of well deviation, the USGS INL Project Office applies a correction factor to water-level data when a well deviation survey indicates a change in the reference elevation of greater than or equal to 0.2 ft.
Borehole well deviation survey data were considered for 177 wells completed within the eastern Snake River Plain aquifer, but not all wells had deviation survey data available. As of 2016, USGS INL Project Office database includes: 57 wells with gyroscopic survey data; 100 wells with magnetic deviation survey data; 11 wells with erroneous gyroscopic data that were excluded; and, 68 wells with no deviation survey data available. Of the 57 wells with gyroscopic deviation surveys, correction factors for 16 wells ranged from 0.20 to 6.07 ft and inclination angles (SANG) ranged from 1.6 to 16.0 degrees. Of the 100 wells with magnetic deviation surveys, a correction factor for 21 wells ranged from 0.20 to 5.78 ft and SANG ranged from 1.0 to 13.8 degrees, not including the wells that did not meet the correction factor criteria of greater than or equal to 0.20 ft.
Forty-seven wells had gyroscopic and magnetic deviation survey data for the same well. Datasets for both survey types were compared for the same well to determine whether magnetic survey data were consistent with gyroscopic survey data. Of those 47 wells, 96 percent showed similar correction factor estimates (≤ 0.20 ft) for both magnetic and gyroscopic well deviation surveys. A linear comparison of correction factor estimates for both magnetic and gyroscopic deviation well surveys for all 47 wells indicate good linear correlation, represented by an r-squared of 0.88. The correction factor difference between the gyroscopic and magnetic surveys for 45 of 47 wells ranged from 0.00 to 0.18 ft, not including USGS 57 and USGS 125. Wells USGS 57 and USGS 125 show a correction factor difference of 2.16 and 0.36 ft, respectively; however, review of the data files suggest erroneous SANG data for both magnetic deviation well surveys. The difference in magnetic and gyroscopic well deviation SANG measurements, for all wells, ranged from 0.0 to 0.9 degrees. These data indicate good agreement between SANG data measured using the magnetic deviation survey methods and SANG data measured using gyroscopic deviation survey methods, even for surveys collected years apart.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165163","collaboration":"DOE/ID-22241Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
http://id.water.usgs.gov
In 2012, the U.S. Geological Survey, in cooperation with the Arkansas River Basin Regional Resource Planning Group, initiated a study of groundwater and surface-water interaction, water quality, and loading of dissolved solids, selenium, and uranium to Fountain Creek near Pueblo, Colorado, to improve understanding of sources and processes affecting loading of these constituents to streams in the Arkansas River Basin. Fourteen monitoring wells were installed in a series of three transects across Fountain Creek near Pueblo, and temporary streamgages were established at each transect to facilitate data collection for the study. Groundwater and surface-water interaction was characterized by using hydrogeologic mapping, groundwater and stream-surface levels, groundwater and stream temperatures, vertical hydraulic-head gradients and ratios of oxygen and hydrogen isotopes in the hyporheic zone, and streamflow mass-balance measurements. Water quality was characterized by collecting periodic samples from groundwater, surface water, and the hyporheic zone for analysis of dissolved solids, selenium, uranium, and other selected constituents and by evaluating the oxidation-reduction condition for each groundwater sample under different hydrologic conditions throughout the study period. Groundwater loads to Fountain Creek and in-stream loads were computed for the study area, and processes affecting loads of dissolved solids, selenium, and uranium were evaluated on the basis of geology, geochemical conditions, land and water use, and evapoconcentration.
During the study period, the groundwater-flow system generally contributed flow to Fountain Creek and its hyporheic zone (as a single system) except for the reach between the north and middle transects. However, the direction of flow between the stream, the hyporheic zone, and the near-stream aquifer was variable in response to streamflow and stage. During periods of low streamflow, Fountain Creek generally gained flow from groundwater. However, during periods of high streamflow, the hydraulic gradient between groundwater and the stream temporarily reversed, causing the stream to lose flow to groundwater.
Concentrations of dissolved solids, selenium, and uranium in groundwater generally had greater spatial variability than surface water or hyporheic-zone samples, and constituent concentrations in groundwater generally were greater than in surface water. Constituent concentrations in the hyporheic zone typically were similar to or intermediate between concentrations in groundwater and surface water. Concentrations of dissolved solids, selenium, uranium, and other constituents in groundwater samples collected from wells located on the east side of the north monitoring well transect were substantially greater than for other groundwater, surface-water, and hyporheic-zone samples. With one exception, groundwater samples collected from wells on the east side of the north transect exhibited oxic to mixed (oxic-anoxic) conditions, whereas most other groundwater samples exhibited anoxic to suboxic conditions. Concentrations of dissolved solids, selenium, and uranium in surface water generally increased in a downstream direction along Fountain Creek from the north transect to the south transect and exhibited an inverse relation to streamflow with highest concentration occurring during periods of low streamflow and lowest concentrations occurring during periods of high streamflow.
Groundwater loads of dissolved solids, selenium, and uranium to Fountain Creek were small because of the small amount of groundwater flowing to the stream under typical low-streamflow conditions. In-stream loads of dissolved solids, selenium, and uranium in Fountain Creek varied by date, primarily in relation to streamflow at each transect and were much larger than computed constituent loads from groundwater. In-stream loads generally decreased with decreases in streamflow and increased as streamflow increased. In-stream loads of dissolved solids and selenium increased between the north and middle transects but generally decreased between the middle and south transects. By contrast, uranium loads generally decreased between the north and middle transects but increased between the middle and south transects. In-stream load differences between transects appear primarily to be related to differences in streamflow. However, because groundwater typically flows to Fountain Creek under low-flow conditions, and groundwater has greater concentrations of dissolved solids, selenium, and uranium than surface water in Fountain Creek, increases in loads between transects likely are affected by inflow of groundwater to the stream, which can account for a substantial proportion of the in-stream load difference between transects. When loads decreased between transects, the primary cause likely was decreased streamflow as a result of losses to groundwater and flow through the hyporheic zone. However, localized groundwater inflow likely attenuated the magnitude by which the in-stream loads decreased.
The combination of localized soluble geologic sources and oxic conditions likely is the primary reason for the occurrence of high concentrations of dissolved solids, selenium, and uranium in groundwater on the east side of the north monitoring well transect. To evaluate conditions potentially responsible for differences in water quality and redox conditions, physical characteristics such as depth to water, saturated thickness, screen depth below the water table, screen height above bedrock, and aquifer hydraulic conductivity were compared by using Wilcoxon rank-sum tests. Results indicated no significant difference between depth to water, screen height above bedrock, and hydraulic conductivity for groundwater samples collected from wells on the east side of the north transect and groundwater samples from all other wells. However, saturated thickness and screen depth below the water table both were significantly smaller for groundwater samples collected from wells on the east side of the north transect than for groundwater samples from other wells, indicating that these characteristics might be related to the elevated constituent concentrations found at that location. Similarly, saturated thickness and screen depth below the water table were significantly smaller for groundwater samples under oxic or mixed (oxic-anoxic) conditions than for those under anoxic to suboxic conditions.
The greater constituent concentrations at wells on the east side of the north transect also could, in part, be related to groundwater discharge from an unnamed alluvial drainage located directly upgradient from that location. Although the quantity and quality of water discharging from the drainage is not known, the drainage appears to collect water from a residential area located upgradient to the east of the wells, and groundwater could become concentrated in nitrate and other dissolved constituents before flowing through the drainage. High levels of nitrate, whether from anthropogenic or natural geologic sources, could promote more soluble forms of selenium and other constituents by affecting the redox condition of groundwater. Whether oxic conditions at wells on the east side of the north transect are the result of physical characteristics or of groundwater inflow from the alluvial drainage, the oxic conditions appear to cause increased dissolution of minerals from the shallow shale bedrock at that location. Because ratios of hydrogen and oxygen isotopes indicate evaporation likely has not had a substantial effect on groundwater, constituent concentrations at that location likely are not the result of evapoconcentration.
Director, USGS Colorado Water Science Center
Box 25046, Mail Stop 415
Denver, CO 80225
The biological and hydraulic performance of a portable floating fish collector (PFFC) located in the cul-de-sac of Cougar Dam and Reservoir, Oregon, was evaluated during 2015–16. The PFFC, first commissioned in May 2014, was modified during winter 2014–15 to address several deficiencies identified during operation and testing in 2014. These modifications included raising the water inflow structures to reduce the depth and volume of inflow to improve the internal hydraulic profiles, and moving the anchors so the PFFC could be positioned closer to the existing reservoir outlet, a water temperature control tower. The PFFC was positioned about 18 meters (m) upstream of the intake of the water temperature control tower and faced into the prevailing water current. Like several floating surface collectors operating in the Pacific Northwest at the time, the PFFC used pumps to draw water and fish over an inclined plane, past dewatering screens, and into a collection area. The portable and experimental nature of the PFFC required a smaller size, shallower entrance (about 2.5-m deep), and smaller inflow rate (72 cubic feet per second [ft3/s] inflow during the Low treatment, 122 ft3/s during the High treatment) than other collectors in the region.
The collection of the target species, juvenile Chinook salmon (Oncorhynchus tshawytscha), during 2015–16 was an order of magnitude larger than in 2014. Subyearling-age Chinook salmon comprised most of the catch (2,616 subyearling compared to 258 yearling) and was greatest during the spring during the High inflow treatment. Bycatch consisted predominantly of cyprinids and centrarchids. Trap mortality (fish found dead in the trap) of juvenile Chinook salmon, at 9.2 percent of the subyearlings and 5.0 percent of yearlings, was about 30 percent of the level in 2014. Fish mortality from handling the live catch was about 1 percent.
Data from fish tagged with passive integrated transponder (PIT) tags and those with acoustic+PIT tags released near the head of the reservoir indicated the catch rates of the PFFC were low. Eight of the 1,497 PIT-tagged fish and 5 of the 534 acoustic+PIT-tagged fish were collected by the PFFC. Fish collection efficiencies—the number collected by the PFFC out of the number detected at the head of the forebay (FCEFB) or in the cul-de-sac (FCECDS)—were 0.002 and 0.003 during the Low treatment and 0.008 and 0.009 during the High treatment. The low FCEs were attributed to the following factors:
The data suggest that the shallow entrance and low inflow rate reduced fish guidance near the PFFC entrance and the hydraulic characteristics resulting from the outflow plumes (and perhaps water entering the temperature control tower) attracted fish to that area. Catch of juvenile Chinook salmon likely would increase if the collector entrance were deepened, the inflow rate were increased, and measures were taken to constrain fish presence to the area upstream of the trap entrance.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161197","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Beeman, J.W., Evans, S.D., Haner, P.V., Hansel, H.C., Hansen, A.C., Hansen, G.S., Hatton, T.W., Kofoot, E.E., and Sprando, J.M., 2016, Evaluation of the biological and hydraulic performance of the portable floating fish collector at Cougar Reservoir and Dam, Oregon, September 2015–January 2016: U.S. Geological Survey Open-File Report 2016–1197, 98 p., https://doi.org/10.3133/ofr20161197.","productDescription":"x, 98 p.","numberOfPages":"112","onlineOnly":"Y","ipdsId":"IP-078812","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":331253,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1197/ofr20161197.pdf","text":"Report","size":"9.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1197"},{"id":331252,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1197/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Cougar Reservoir and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.2507095336914,\n 44.065386786862234\n ],\n [\n -122.2507095336914,\n 44.13023159235851\n ],\n [\n -122.20401763916016,\n 44.13023159235851\n ],\n [\n -122.20401763916016,\n 44.065386786862234\n ],\n [\n -122.2507095336914,\n 44.065386786862234\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
http://wfrc.usgs.gov/
This study produced a geospatial database for use in a decision support system by the Bolivian authorities to investigate further development and investment potentials in sustainable hydropower in Bolivia. The study assessed theoretical hydropower of all 1-kilometer (km) stream segments in the country using multisource satellite data and a hydrologic modeling approach. With the assessment covering the 2 million square kilometer (km2) region influencing Bolivia’s drainage network, the potential hydropower figures are based on theoretical yield assuming that the systems generating the power are 100 percent efficient. There are several factors to consider when determining the real-world or technical power potential of a hydropower system, and these factors can vary depending on local conditions. Since this assessment covers a large area, it was necessary to reduce these variables to the two that can be modeled consistently throughout the region, streamflow or discharge, and elevation drop or head. First, the Shuttle Radar Topography Mission high-resolution 30-meter (m) digital elevation model was used to identify stream segments with greater than 10 km2 of upstream drainage. We applied several preconditioning processes to the 30-m digital elevation model to reduce errors and improve the accuracy of stream delineation and head height estimation. A total of 316,500 1-km stream segments were identified and used in this study to assess the total theoretical hydropower potential of Bolivia. Precipitation observations from a total of 463 stations obtained from the Bolivian Servicio Nacional de Meteorología e Hidrología (Bolivian National Meteorology and Hydrology Service) and the Brazilian Agência Nacional de Águas (Brazilian National Water Agency) were used to validate six different gridded precipitation estimates for Bolivia obtained from various sources. Validation results indicated that gridded precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) reanalysis product (3B43) had the highest accuracies. The coarse-resolution (25-km) TRMM data were disaggregated to 5-km pixels using climatology information obtained from the Climate Hazards Group Infrared Precipitation with Stations dataset. About a 17-percent bias was observed in the disaggregated TRMM estimates, which was corrected using the station observations. The bias-corrected, disaggregated TRMM precipitation estimate was used to compute stream discharge using a regionalization approach. In regionalization approach, required homogeneous regions for Bolivia were derived from precipitation patterns and topographic characteristics using a k-means clustering approach. Using the discharge and head height estimates for each 1-km stream segment, we computed hydropower potential for 316,490 stream segments within Bolivia and that share borders with Bolivia. The total theoretical hydropower potential (TTHP) of these stream segments was found to be 212 gigawatts (GW). Out of this total, 77.4 GW was within protected areas where hydropower projects cannot be developed; hence, the remaining total theoretical hydropower in Bolivia (outside the protected areas) was estimated as 135 GW. Nearly 1,000 1-km stream segments, however, were within the boundaries of existing hydropower projects. The TTHP of these stream segments was nearly 1.4 GW, so the residual TTHP of the streams in Bolivia was estimated as 133 GW. Care should be exercised to understand and interpret the TTHP identified in this study because all the stream segments identified and assessed in this study cannot be harnessed to their full capacity; furthermore, factors such as required environmental flows, efficiency, economics, and feasibility need to be considered to better identify a more real-world hydropower potential. If environmental flow requirements of 20–40 percent are considered, the total theoretical power available reduces by 60–80 percent. In addition, a 0.72 efficiency factor further reduces the estimation by another 28 percent. This study provides the base theoretical hydropower potential for Bolivia, the next step is to identify optimal hydropower plant locations and factor in the principles to appraise a real-world power potential in Bolivia.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161156","collaboration":"Prepared in cooperation with the CAF – Development Bank of Latin America","usgsCitation":"Velpuri, N.M., Pervez, M.S., and Cushing, W.M., 2016, Hydropower assessment of Bolivia—A multisource satellite data and hydrologic modeling approach: U.S. Geological Survey Open-File Report 2016–1156, 65 p., https://dx.doi.org/10.3133/ofr20161156.","productDescription":"Report: x, 65 p.; Appendixes: 2-4","numberOfPages":"79","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075626","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":331175,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1156/ofr20161156.pdf","text":"Report","size":"23.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1156"},{"id":331174,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1156/coverthb.jpg"},{"id":331176,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2016/1156/downloads","text":"Appendixes 2–4","description":"OFR 2016–1156 Appendixes 2–4"}],"country":"Bolivia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-62.84647,-22.03499],[-63.98684,-21.99364],[-64.37702,-22.79809],[-64.96489,-22.07586],[-66.27334,-21.83231],[-67.10667,-22.73592],[-67.82818,-22.87292],[-68.21991,-21.49435],[-68.75717,-20.37266],[-68.44223,-19.40507],[-68.96682,-18.98168],[-69.10025,-18.26013],[-69.59042,-17.58001],[-68.95964,-16.5007],[-69.38976,-15.66013],[-69.16035,-15.32397],[-69.33953,-14.9532],[-68.94889,-14.45364],[-68.92922,-13.60268],[-68.88008,-12.89973],[-68.66508,-12.5613],[-69.52968,-10.95173],[-68.78616,-11.03638],[-68.27125,-11.01452],[-68.04819,-10.71206],[-67.1738,-10.30681],[-66.64691,-9.93133],[-65.33844,-9.76199],[-65.44484,-10.51145],[-65.3219,-10.89587],[-65.40228,-11.56627],[-64.31635,-12.46198],[-63.1965,-12.62703],[-62.80306,-13.00065],[-62.12708,-13.19878],[-61.7132,-13.4892],[-61.08412,-13.47938],[-60.5033,-13.77595],[-60.4592,-14.35401],[-60.26433,-14.64598],[-60.25115,-15.07722],[-60.54297,-15.09391],[-60.15839,-16.25828],[-58.24122,-16.29957],[-58.38806,-16.87711],[-58.2808,-17.27171],[-57.73456,-17.55247],[-57.49837,-18.17419],[-57.67601,-18.96184],[-57.95,-19.4],[-57.8538,-19.97],[-58.16639,-20.1767],[-58.18347,-19.8684],[-59.11504,-19.35691],[-60.04356,-19.34275],[-61.78633,-19.63374],[-62.26596,-20.51373],[-62.29118,-21.05163],[-62.68506,-22.24903],[-62.84647,-22.03499]]]},\"properties\":{\"name\":\"Bolivia\"}}]}","contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Snakes have become successful invaders in a wide variety of ecosystems worldwide. In southern Florida, USA, the Burmese python (Python molurus bivittatus) has become established across thousands of square kilometers including all of Everglades National Park (ENP). Both experimental and correlative data have supported a relationship between Burmese python predation and declines or extirpations of mid- to large-sized mammals in ENP. In June 2013 a large python (4.32 m snout-vent length, 48.3 kg) was captured and removed from the park. Subsequent necropsy revealed a massive amount of fecal matter (79 cm in length, 6.5 kg) within the snake’s large intestine. A comparative examination of bone, teeth, and hooves extracted from the fecal contents revealed that this snake consumed three white-tailed deer (Odocoileus virginianus). This is the first report of an invasive Burmese python containing the remains of multiple white-tailed deer in its gut. Because the largest snakes native to southern Florida are not capable of consuming even mid-sized mammals, pythons likely represent a novel predatory threat to white-tailed deer in these habitats. This work highlights the potential impact of this large-bodied invasive snake and supports the need for more work on invasive predator-native prey relationships.
","language":"English","publisher":"REABIC","doi":"10.3391/bir.2016.5.4.02","usgsCitation":"Boback, S.M., Snow, R.W., Hsu, T., Peurach, S.C., Dove, C.J., and Reed, R., 2016, Supersize me: Remains of three white-tailed deer (Odocoileus virginianus) in an invasive Burmese python (Python molurus bivittatus) in Florida : BioInvasions Records, v. 5, no. 4, p. 197-203, https://doi.org/10.3391/bir.2016.5.4.02.","productDescription":"7 p.","startPage":"197","endPage":"203","ipdsId":"IP-072146","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":462027,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/bir.2016.5.4.02","text":"Publisher Index Page"},{"id":331233,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583d502ce4b0d9329c80c58f","contributors":{"authors":[{"text":"Boback, Scott M.","contributorId":69370,"corporation":false,"usgs":false,"family":"Boback","given":"Scott","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":654318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Snow, Ray W.","contributorId":76449,"corporation":false,"usgs":false,"family":"Snow","given":"Ray","email":"","middleInitial":"W.","affiliations":[{"id":13415,"text":"Everglades National Park","active":true,"usgs":false}],"preferred":false,"id":654319,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hsu, Teresa","contributorId":177027,"corporation":false,"usgs":false,"family":"Hsu","given":"Teresa","email":"","affiliations":[],"preferred":false,"id":654320,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peurach, Suzanne C. speurach@usgs.gov","contributorId":3064,"corporation":false,"usgs":true,"family":"Peurach","given":"Suzanne","email":"speurach@usgs.gov","middleInitial":"C.","affiliations":[],"preferred":true,"id":654321,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dove, Carla J.","contributorId":98577,"corporation":false,"usgs":true,"family":"Dove","given":"Carla","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":654322,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reed, Robert N. reedr@usgs.gov","contributorId":1686,"corporation":false,"usgs":true,"family":"Reed","given":"Robert N.","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":654323,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178003,"text":"ofr20161178 - 2016 - Facilitating the inclusion of nonmarket values in Bureau of Land Management planning and project assessments—Final report","interactions":[],"lastModifiedDate":"2016-11-23T11:24:07","indexId":"ofr20161178","displayToPublicDate":"2016-11-23T11:40:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1178","title":"Facilitating the inclusion of nonmarket values in Bureau of Land Management planning and project assessments—Final report","docAbstract":"This report summarizes the results of a series of field-based case studies conducted by the U.S. Geological Survey (USGS) to (1) evaluate the use of nonmarket values in Bureau of Land Management (BLM) planning and project assessments, (2) update existing technical resources for measuring those values, and (3) provide guidance to field staff on the use of nonmarket values. Four BLM pilot sites participated in this effort: Canyons of the Ancients National Monument in Colorado, Red Cliffs and Beaver Dam Wash National Conservation Areas in Utah, BLM’s Taos Field Office in New Mexico, and BLM's Tuscarora Field Office in Nevada. The focus of the case studies was on practical applications of nonmarket valuation. USGS worked directly with BLM field staff at the pilot sites to demonstrate the process of considering nonmarket values in BLM decisionmaking and document the questions, challenges, and opportunities that arise when tying economic language to projects.
As part of this effort, a Web-based toolkit, available at https://my.usgs.gov/benefit-transfer/, was updated and expanded to help facilitate benefit transfers (that is, the use of existing economic data to quantify nonmarket values) and qualitative discussions of nonmarket values. A total of 53 new or overlooked nonmarket valuation studies comprising 494 nonmarket value estimates for various recreational activities and the preservation of threatened, endangered, and rare species were added to existing databases within this Benefit Transfer Toolkit. In addition, four meta-regression functions focused on hunting, wildlife viewing, fishing, and trail use recreation were developed and added to the Benefit Transfer Toolkit.
Results of this effort demonstrate that there are two main roles for nonmarket valuation in BLM planning. The first is to improve the decisionmaking process by contributing to a more comprehensive comparison of economic benefits and cost when evaluating resource tradeoffs for National Environmental Policy Act analyses. The second is to use economic language and information on economic values, either qualitative or quantitative, to improve the ability to communicate the economic significance of the resources provided by BLM-managed lands.
Findings also indicate that the use of existing economic data to quantify nonmarket values (that is, benefit transfer) poses unique challenges because of the scarcity of both resource data and existing valuation studies focused on resources and sites managed by BLM. This highlights the need for improvements in the collection of resource data at BLM sites, especially visitor use data, as well as an opportunity for BLM’s Socioeconomics Program to strategically identify priority areas, in terms of both resources and geographic locations, where primary valuation studies could be conducted and the results used for future benefit transfers. Finally, whereas qualitative discussions of nonmarket values do not facilitate the comparison of monetized values, they can provide a manageable next step forward in providing more comprehensive information on nonmarket values for BLM plans and project assessments.
Center Director, USGS Fort Collins Science Center
2150 Centre Ave., Bldg. C
Box 25046, MS-939
Fort Collins, CO 80526-8118
A data set was acquired on a shallow mudflat in south San Francisco Bay that featured simultaneous, co-located optical and acoustic sensors for subsequent estimation of suspended sediment concentrations (SSC). The optical turbidity sensor output was converted to SSC via an empirical relation derived at a nearby site using bottle sample estimates of SSC. The acoustic data was obtained using an acoustic Doppler velocimeter. Backscatter and noise were combined to develop another empirical relation between the optical estimates of SSC and the relative backscatter from the acoustic velocimeter. The optical and acoustic approaches both reproduced similar general trends in the data and have merit. Some seasonal variation in the dataset was evident, with the two methods differing by greater or lesser amounts depending on which portion of the record was examined. It is hypothesized that this is the result of flocculation, affecting the two signals by different degrees, and that the significance or mechanism of the flocculation has some seasonal variability. In the earlier portion of the record (March), there is a clear difference that appears in the acoustic approach between ebb and flood periods, and this is not evident later in the record (May). The acoustic method has promise but it appears that characteristics of flocs that form and break apart may need to be accounted for to improve the power of the method. This may also be true of the optical method: both methods involve assuming that the sediment characteristics (size, size distribution, and shape) are constant.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings of the 35th International Conference on Coastal Engineering","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"conferenceTitle":"35th International Conference on Coastal Engineering","conferenceDate":"November 17-20, 2016","conferenceLocation":"Antalya, Turkey","language":"English","usgsCitation":"Öztürk, M., and Work, P.A., 2016, Acoustic Doppler velocimeter backscatter for quantification of suspended sediment concentration in South San Francisco Bay, USA, in Proceedings of the 35th International Conference on Coastal Engineering, Antalya, Turkey, November 17-20, 2016, 13 p.","productDescription":"13 p.","ipdsId":"IP-068263","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":340083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"South San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.00292968749999,\n 37.31775185163688\n ],\n [\n -121.84936523437499,\n 37.31775185163688\n ],\n [\n -121.84936523437499,\n 38.156156969924915\n ],\n [\n -123.00292968749999,\n 38.156156969924915\n ],\n [\n -123.00292968749999,\n 37.31775185163688\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4ce4b0c3010a8087b9","contributors":{"authors":[{"text":"Öztürk, Mehmet mozturk@usgs.gov","contributorId":168560,"corporation":false,"usgs":true,"family":"Öztürk","given":"Mehmet","email":"mozturk@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":692415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":626466,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70178529,"text":"70178529 - 2016 - Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative","interactions":[],"lastModifiedDate":"2017-01-17T19:03:06","indexId":"70178529","displayToPublicDate":"2016-11-23T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative","docAbstract":"The U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) initiative is a\nnew end-to-end capability to continuously track and characterize changes in land cover, use, and condition\nto better support research and applications relevant to resource management and environmental\nchange. Among the LCMAP product suite are annual land cover maps that will be available to the public.\nThis paper describes an approach to optimize the selection of training and auxiliary data for deriving the\nthematic land cover maps based on all available clear observations from Landsats 4–8. Training data were\nselected from map products of the U.S. Geological Survey’s Land Cover Trends project. The Random Forest\nclassifier was applied for different classification scenarios based on the Continuous Change Detection and\nClassification (CCDC) algorithm. We found that extracting training data proportionally to the occurrence\nof land cover classes was superior to an equal distribution of training data per class, and suggest using a\ntotal of 20,000 training pixels to classify an area about the size of a Landsat scene. The problem of unbalanced\ntraining data was alleviated by extracting a minimum of 600 training pixels and a maximum of\n8000 training pixels per class. We additionally explored removing outliers contained within the training\ndata based on their spectral and spatial criteria, but observed no significant improvement in classification\nresults. We also tested the importance of different types of auxiliary data that were available for the conterminous\nUnited States, including: (a) five variables used by the National Land Cover Database, (b) three\nvariables from the cloud screening ‘‘Function of mask” (Fmask) statistics, and (c) two variables from the\nchange detection results of CCDC. We found that auxiliary variables such as a Digital Elevation Model and\nits derivatives (aspect, position index, and slope), potential wetland index, water probability, snow probability,\nand cloud probability improved the accuracy of land cover classification. Compared to the original\nstrategy of the CCDC algorithm (500 pixels per class), the use of the optimal strategy improved the classification\naccuracies substantially (15-percentage point increase in overall accuracy and 4-percentage\npoint increase in minimum accuracy).","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam, Netherlands","doi":"10.1016/j.isprsjprs.2016.11.004","usgsCitation":"Zhu, Z., Gallant, A.L., Woodcock, C., Pengra, B., Olofsson, P., Loveland, T., Jin, S., Dahal, D., Yang, L., and Auch, R.F., 2016, Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative: ISPRS Journal of Photogrammetry and Remote Sensing, v. 122, p. 206-221, https://doi.org/10.1016/j.isprsjprs.2016.11.004.","productDescription":"16 p.","startPage":"206","endPage":"221","numberOfPages":"16","ipdsId":"IP-080672","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2016.11.004","text":"Publisher Index Page"},{"id":331219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"122","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5836b8dde4b0d9329c801c53","contributors":{"authors":[{"text":"Zhu, Zhe 0000-0001-8283-6407 zhezhu@usgs.gov","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":168792,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhe","email":"zhezhu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654293,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654287,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodcock, Curtis","contributorId":166666,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":654502,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pengra, Bruce 0000-0003-2497-8284 bpengra@usgs.gov","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":5132,"corporation":false,"usgs":true,"family":"Pengra","given":"Bruce","email":"bpengra@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654291,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Olofsson, Pontus","contributorId":131007,"corporation":false,"usgs":false,"family":"Olofsson","given":"Pontus","email":"","affiliations":[{"id":7208,"text":"Department of Earth and Environment, Boston University","active":true,"usgs":false}],"preferred":false,"id":654290,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Loveland, Thomas R. 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":121503,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas R.","affiliations":[],"preferred":false,"id":654289,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jin, Suming 0000-0001-9919-8077 sjin@usgs.gov","orcid":"https://orcid.org/0000-0001-9919-8077","contributorId":4397,"corporation":false,"usgs":true,"family":"Jin","given":"Suming","email":"sjin@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654288,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":654286,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yang, Limin 0000-0002-2843-6944 lyang@usgs.gov","orcid":"https://orcid.org/0000-0002-2843-6944","contributorId":4305,"corporation":false,"usgs":true,"family":"Yang","given":"Limin","email":"lyang@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654292,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654285,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70188065,"text":"70188065 - 2016 - Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data","interactions":[],"lastModifiedDate":"2017-05-31T16:04:59","indexId":"70188065","displayToPublicDate":"2016-11-23T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data","docAbstract":"There are many types of changes occurring over the Earth's landscapes that can be detected and monitored using Landsat data. Here we focus on monitoring “within-state,” gradual changes in vegetation in contrast with traditional monitoring of “abrupt” land-cover conversions. Gradual changes result from a variety of processes, such as vegetation growth and succession, damage from insects and disease, responses to shifts in climate, and other factors. Despite the prevalence of gradual changes across the landscape, they are largely ignored by the remote sensing community. Gradual changes are best characterized and monitored using time-series analysis, and with the successful launch of Landsat 8 we now have appreciable data continuity that extends the Landsat legacy across the previous 43 years. In this study, we conducted three related analyses: (1) comparison of spectral values acquired by Landsats 7 and 8, separated by eight days, to ensure compatibility for time-series evaluation; (2) tracking of multitemporal signatures for different change processes across Landsat 5, 7, and 8 sensors using anniversary-date imagery; and (3) tracking the same type of processes using all available acquisitions. In this investigation, we found that data representing natural vegetation from Landsats 5, 7, and 8 were comparable and did not indicate a need for major modification prior to use for long-term monitoring. Analyses using anniversary-date imagery can be very effective for assessing long term patterns and trends occurring across the landscape, and are especially good for providing insights regarding trends related to long-term and continuous trends of growth or decline. We found that use of all available data provided a much more comprehensive level of understanding of the trends occurring, providing information about rate, duration, and intra- and inter-annual variability that could not be readily gleaned from the anniversary date analyses. We observed that using all available clear Landsat 5–8 observations with the new Continuous Change Detection and Classification (CCDC) algorithm was very effective for illuminating vegetation trends. There are a number of potential challenges for assessing gradual changes, including atmospheric impacts, algorithm development and visualization of the changes. One of the biggest challenges for studying gradual change will be the lack of appropriate data for validating results and products.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.02.060","usgsCitation":"Vogelmann, J., Gallant, A.L., Shi, H., and Zhu, Z., 2016, Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data: Remote Sensing of Environment, v. 185, p. 258-270, https://doi.org/10.1016/j.rse.2016.02.060.","productDescription":"13 p.","startPage":"258","endPage":"270","ipdsId":"IP-066052","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470406,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.02.060","text":"Publisher Index Page"},{"id":341856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84b8e4b092b266f10d2c","contributors":{"authors":[{"text":"Vogelmann, James 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":192352,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":696377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":696379,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhe 0000-0001-8283-6407 zhezhu@usgs.gov","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":168792,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhe","email":"zhezhu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696380,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70176879,"text":"sir20165137 - 2016 - Hydrogeology and hydrologic conditions of the Ozark Plateaus aquifer system","interactions":[],"lastModifiedDate":"2016-11-29T10:22:40","indexId":"sir20165137","displayToPublicDate":"2016-11-23T00:00:00","publicationYear":"2016","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":"2016-5137","title":"Hydrogeology and hydrologic conditions of the Ozark Plateaus aquifer system","docAbstract":"The hydrogeology and hydrologic characteristics of the Ozark Plateaus aquifer system were characterized as part of ongoing U.S. Geological Survey efforts to assess groundwater availability across the Nation. The need for such a study in the Ozark Plateaus physiographic province (Ozark Plateaus) is highlighted by increasing demand on groundwater resources by the 5.3 million people of the Ozark Plateaus, water-level declines in some areas, and potential impacts of climate change on groundwater availability. The subject study integrates knowledge gained through local investigation within a regional perspective to develop a regional conceptual model of groundwater flow in the Ozark Plateaus aquifer system (Ozark system), a key phase of groundwater availability assessment. The Ozark system extends across much of southern Missouri and northwestern and north-central Arkansas and smaller areas of southeastern Kansas and northeastern Oklahoma. The region is one of the major karst landscapes in the United States, and karst aquifers are predominant in the Ozark system. Groundwater flow is ultimately controlled by aquifer and confining unit lithologies and stratigraphic relations, geologic structure, karst development, and the character of surficial lithologies and regolith mantle. The regolith mantle is a defining element of Ozark Plateaus karst, affecting recharge, karst development, and vulnerability to surface-derived contaminants. Karst development is more advanced—as evidenced by larger springs, hydraulic characteristics, and higher well yields—in the Salem Plateau and in the northern part of the Springfield Plateau (generally north of the Arkansas-Missouri border) as compared with the southern part of the Springfield Plateau in Arkansas, largely due to thinner, less extensive regolith and purer carbonate lithology.
Precipitation is the ultimate source of all water to the Ozark system, and the hydrologic budget for the Ozark system includes inputs from recharge, losing-stream sections, and groundwater inflows and losses of water to gaining-stream sections, groundwater withdrawals, and surface-water and groundwater outflows to neighboring systems. Groundwater recharge, estimated by a soil-water-balance model, represents about 24 percent, or 11 inches, of 43.9 inches annual precipitation. Recharge is spatially variable, being greater in the northern Springfield Plateau and Salem Plateau than in the southern Springfield Plateau (generally south of the Arkansas border) because of differences in regolith mantle extent and thickness and carbonate lithology and hydraulic properties. Increased precipitation and decreased agricultural land use during the period 1951 through 2011 increased recharge by approximately 5 percent. Although all Ozark streams have losing, neutral, and gaining sections, they are dominantly gaining and are a net sink for groundwater with nearly 90 percent of groundwater recharge returned to springs and streams. Groundwater pumping is a small but important loss of water in the Ozark system hydrologic budget; water-level declines and local cones of depression have been observed around pumping centers and strong concerns exist over potential effects on stream and spring flow.
Data indicate that societal needs for freshwater resources in the Ozark Plateaus will continue to increase and will do so in the context of changing climate and hydrology. Groundwater will continue to be an important part of supporting these societal needs and also local ecosystems. The unique character and hydrogeologic variability across the Ozark system will control how the system responds to future stress. Groundwater of the Ozark system in the northern study area is more dynamic, has greater storage and larger flux, and has greater potential for further development than in the part of the study area south of the Arkansas-Missouri border. Further south in Arkansas, a line exists, roughly defined as 5 miles south of the Springfield Plateau-Boston Mountains boundary, beyond which further extensive municipal or commercial development appears unlikely under current economic and resource-need conditions. A small part of the Ozark system groundwater budget is currently drafted for use, leaving an apparently large component available for further development and use—particularly in the northern Springfield Plateau and Salem Plateau; however, the effects of increased pumping on groundwater’s role in maintaining ecosystems and ecosystem services are not quantitatively well understood, and the close relation between groundwater and surface water highlights the importance of further quantitative assessment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165137","collaboration":"Prepared in cooperation with the Groundwater Resources Program","usgsCitation":"Hays, P.D., Knierim, K.J., Breaker, Brian, Westerman, D.A., and Clark, B.R., 2016, Hydrogeology and hydrologic conditions of the Ozark Plateaus aquifer system: U.S. Geological Survey Scientific Investigations Report 2016–5137, 61 p., https://dx.doi.org/10.3133/sir20165137. \n\n","productDescription":"Report: vii, 61 p.; Appendixes: 1-2","numberOfPages":"73","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071467","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":331147,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5137/coverthb.jpg"},{"id":331148,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5137/sir20165137.pdf","description":"SIR 2016–5137"},{"id":331149,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5137/downloads","text":"Appendix 1 & 2","description":"SIR 2016–5137 Appendix 1 & 2"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark Plateaus Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.1263427734375,\n 38.81831117374662\n ],\n [\n -90.3076171875,\n 38.89103282648846\n ],\n [\n -90.4559326171875,\n 38.8225909761771\n ],\n [\n -90.692138671875,\n 38.70694605159386\n ],\n [\n -90.8734130859375,\n 38.556757147352215\n ],\n [\n -91.0546875,\n 38.59970036588819\n ],\n [\n -91.2689208984375,\n 38.66835610151506\n ],\n [\n -91.3897705078125,\n 38.71551876930462\n ],\n [\n -91.571044921875,\n 38.68122173079789\n ],\n [\n -91.7083740234375,\n 38.70265930723801\n ],\n [\n -91.9171142578125,\n 38.65119833229951\n ],\n [\n -92.07092285156249,\n 38.57823196583313\n ],\n [\n -92.197265625,\n 38.586820096127674\n ],\n [\n -92.3345947265625,\n 38.6897975322717\n ],\n [\n -92.4884033203125,\n 38.90813299596705\n ],\n [\n -92.57080078125,\n 38.96368010198575\n ],\n [\n -92.74108886718749,\n 38.98503278695909\n ],\n [\n -92.87841796875,\n 38.997841307500714\n ],\n [\n -92.92236328125,\n 39.091699613104595\n ],\n [\n -92.9058837890625,\n 39.17691709496078\n ],\n [\n -92.8619384765625,\n 39.2407625100131\n ],\n [\n -92.9608154296875,\n 39.317300373271024\n ],\n [\n -93.0926513671875,\n 39.38101803294523\n ],\n [\n -93.2025146484375,\n 39.40224434029275\n ],\n [\n -93.350830078125,\n 39.30454987014581\n ],\n [\n -93.482666015625,\n 39.287545585410435\n ],\n [\n -93.70788574218749,\n 39.223742741391305\n ],\n [\n -93.98803710937499,\n 39.15136267949029\n ],\n [\n -95.2349853515625,\n 38.25112269630296\n ],\n [\n -95.2789306640625,\n 36.45221769643571\n ],\n [\n -95.0152587890625,\n 35.30840140169162\n ],\n [\n -94.19128417968749,\n 35.35321610123823\n ],\n [\n -93.834228515625,\n 35.47409160773029\n ],\n [\n -93.482666015625,\n 35.40696093270201\n ],\n [\n -93.16955566406249,\n 35.25459097465022\n ],\n [\n -93.065185546875,\n 35.15584570226544\n ],\n [\n -92.6641845703125,\n 35.08845057036537\n ],\n [\n -92.55432128906249,\n 34.95349314197422\n ],\n [\n -92.427978515625,\n 34.836349990763864\n ],\n [\n -92.1148681640625,\n 34.8183131456094\n ],\n [\n -90.3515625,\n 35.97800618085566\n ],\n [\n -89.35729980468749,\n 37.01132594307015\n ],\n [\n -89.4561767578125,\n 37.25656608611523\n ],\n [\n -89.4287109375,\n 37.37015718405753\n ],\n [\n -89.46716308593749,\n 37.45741810262938\n ],\n [\n -89.5166015625,\n 37.58811876638322\n ],\n [\n -89.56054687499999,\n 37.71859032558816\n ],\n [\n -89.70886230468749,\n 37.82280243352756\n ],\n [\n -89.8187255859375,\n 37.900865092570065\n ],\n [\n -89.9560546875,\n 37.97451499202459\n ],\n [\n -90.164794921875,\n 38.07836562996712\n ],\n [\n -90.2801513671875,\n 38.14751758025121\n ],\n [\n -90.3680419921875,\n 38.268375880204744\n ],\n [\n -90.32409667968749,\n 38.40194908237822\n ],\n [\n -90.252685546875,\n 38.53097889440024\n ],\n [\n -90.2032470703125,\n 38.62974534092597\n ],\n [\n -90.19775390625,\n 38.70694605159386\n ],\n [\n -90.1263427734375,\n 38.81831117374662\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
Information concerning the availability, use, and quality of water in West Baton Rouge Parish, Louisiana, is critical for proper water-resource management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. Information on the availability, past and current use, use trends, and water quality from groundwater and surface-water sources in the parish is presented. Previously published reports and data stored in the U.S. Geological Survey’s National Water Information System (http://waterdata.usgs.gov/nwis) are the primary sources of the information presented here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163068","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., and Prakken, L.B., 2016, Water resources of West Baton Rouge Parish, Louisiana: U.S. Geological Survey Fact Sheet 2016–3068, 6 p., https://dx.doi.org/10.3133/fs20163068.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-073099","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":330988,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3068/coverthb.jpg"},{"id":330989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3068/fs20163068.pdf","text":"Fact Sheet","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3068"}],"country":"United States","state":"Louisiana","county":"West Baton Rouge Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.3051,30.6529],[-91.2993,30.6516],[-91.2977,30.6493],[-91.2972,30.6401],[-91.2999,30.631],[-91.3057,30.6182],[-91.3132,30.6018],[-91.3164,30.5903],[-91.3159,30.5816],[-91.3117,30.5752],[-91.3085,30.5739],[-91.2947,30.5711],[-91.2666,30.571],[-91.2502,30.5632],[-91.2459,30.5559],[-91.2438,30.55],[-91.2444,30.5454],[-91.246,30.5395],[-91.2588,30.5294],[-91.2811,30.5189],[-91.2848,30.5158],[-91.2843,30.5098],[-91.28,30.5052],[-91.2684,30.5047],[-91.2503,30.5097],[-91.2094,30.5229],[-91.201,30.5178],[-91.1973,30.5073],[-91.196,30.4396],[-91.1997,30.42],[-91.2152,30.3939],[-91.2338,30.3757],[-91.2412,30.362],[-91.2418,30.3579],[-91.236,30.3446],[-91.2307,30.3414],[-91.2228,30.3409],[-91.2042,30.3454],[-91.1873,30.3468],[-91.1619,30.3421],[-91.1508,30.3375],[-91.145,30.3315],[-91.1419,30.3237],[-91.3144,30.3246],[-91.3202,30.3443],[-91.3371,30.3526],[-91.3714,30.3874],[-91.3947,30.3956],[-91.3947,30.4094],[-91.4127,30.4322],[-91.4143,30.4318],[-91.4524,30.4743],[-91.4535,30.4753],[-91.4604,30.4707],[-91.4853,30.4972],[-91.4815,30.4972],[-91.4821,30.5114],[-91.4147,30.5118],[-91.4152,30.5191],[-91.4147,30.5255],[-91.4147,30.5406],[-91.4078,30.5406],[-91.4056,30.5557],[-91.4009,30.5621],[-91.3993,30.569],[-91.3977,30.569],[-91.3945,30.569],[-91.3648,30.5689],[-91.3653,30.579],[-91.3653,30.5845],[-91.3653,30.5877],[-91.3631,30.59],[-91.3498,30.6041],[-91.3355,30.616],[-91.3201,30.6329],[-91.3174,30.637],[-91.312,30.6484],[-91.3338,30.6539],[-91.3312,30.6585],[-91.3051,30.6529]]]},\"properties\":{\"name\":\"West Baton Rouge\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120,
Baton Rouge, LA 70816
The U.S. Geological Survey, in cooperation with the Tennessee Department of Environment and Conservation (TDEC), measured continuous discharge at 4 water-quality monitoring sites and developed stage-discharge ratings for 10 additional water-quality monitoring sites in the Elk River Basin during 2006 through 2008. The discharge data were collected to support stream load assessments by TDEC. Annual nitrogen and phosphorus loads were estimated for the four sites where continuous daily discharge records were collected. Reported loads for the period 2006 through 2008 are not representative of long-term mean annual conditions at the sites in this study, however, because of severe drought conditions in the Elk River Basin during this period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1015","collaboration":"Prepared in cooperation with the Tennessee Department of Environment and Conservation","usgsCitation":"Hoos, A.B., Williams, S.D., and Wolfe, W.J., 2016, Stage-discharge relations and annual nitrogen and phosphorus load estimates for stream sites in the Elk River Basin, 2006–2008: U.S. Geological Survey Data Series 1015, 9 p., https://dx.doi.org/10.3133/ds1015.","productDescription":"Report: v, 9 p.; Table 3","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-041936","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":331068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1015/coverthb.jpg"},{"id":331069,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1015/ds1015.pdf","text":"Report","size":"753 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Data Series 1015"},{"id":331070,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/1015/ds1015_table3.xlsx","text":"Table 3 ","size":"101 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 3","linkHelpText":"- Stage-discharge ratings for 10 partial-record stage-discharge sites in the Elk River Basin"}],"country":"United States","state":"Alabama, Tennessee","otherGeospatial":"Elk River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.93505859374999,\n 35.94243575255426\n ],\n [\n -85.5615234375,\n 35.902399875143615\n ],\n [\n -85.53955078125,\n 35.86679541749027\n ],\n [\n -85.5230712890625,\n 35.82672127366604\n ],\n [\n -85.5230712890625,\n 35.746512259918504\n ],\n [\n -85.5340576171875,\n 35.64836915737426\n ],\n [\n -85.5670166015625,\n 35.53222622770337\n ],\n [\n -85.58349609375,\n 35.429344044107154\n ],\n [\n -85.6439208984375,\n 35.32633026307483\n ],\n [\n -85.74829101562499,\n 35.178298352001214\n ],\n [\n -85.9130859375,\n 35.03449433167976\n ],\n [\n -86.0888671875,\n 34.912962495216966\n ],\n [\n -86.253662109375,\n 34.8183131456094\n ],\n [\n -86.5008544921875,\n 34.72355492704221\n ],\n [\n -86.90185546874999,\n 34.615126683462194\n ],\n [\n -87.044677734375,\n 34.610605760914666\n ],\n [\n -87.264404296875,\n 34.56538299699511\n ],\n [\n -87.3907470703125,\n 34.615126683462194\n ],\n [\n -87.451171875,\n 34.72355492704221\n ],\n [\n -87.4896240234375,\n 34.854382885097905\n ],\n [\n -87.462158203125,\n 35.0254981588326\n ],\n [\n -87.418212890625,\n 35.19625600786368\n ],\n [\n -87.4072265625,\n 35.483038134069574\n ],\n [\n -87.2589111328125,\n 35.545635932499415\n ],\n [\n -87.1160888671875,\n 35.66622234103479\n ],\n [\n -86.934814453125,\n 35.75988604933661\n ],\n [\n -86.80847167968749,\n 35.80444911191491\n ],\n [\n -86.5283203125,\n 35.86679541749027\n ],\n [\n -85.93505859374999,\n 35.94243575255426\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Lower Mississippi-Gulf Water Science Center-Tennessee
640 Grassmere Park
Suite 100
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Truncated distributions of pelagophilic fishes have been observed across the Great Plains of North America, with water use and landscape fragmentation implicated as contributing factors. Developing conservation strategies for these species is hindered by the existence of multiple competing flow regime hypotheses related to species persistence. Our primary study objective was to compare the predicted distributions of one pelagophil, the Arkansas River Shiner Notropis girardi, constructed using different flow regime metrics. Further, we investigated different approaches for improving temporal transferability of the species distribution model (SDM). We compared four hypotheses: mean annual flow (a baseline), the 75th percentile of daily flow, the number of zero-flow days, and the number of days above 55th percentile flows, to examine the relative importance of flows during the spawning period. Building on an earlier SDM, we added covariates that quantified wells in each catchment, point source discharges, and non-native species presence to a structured variable framework. We assessed the effects on model transferability and fit by reducing multicollinearity using Spearman’s rank correlations, variance inflation factors, and principal component analysis, as well as altering the regularization coefficient (β) within MaxEnt. The 75th percentile of daily flow was the most important flow metric related to structuring the species distribution. The number of wells and point source discharges were also highly ranked. At the default level of β, model transferability was improved using all methods to reduce collinearity; however, at higher levels of β, the correlation method performed best. Using β = 5 provided the best model transferability, while retaining the majority of variables that contributed 95% to the model. This study provides a workflow for improving model transferability and also presents water-management options that may be considered to improve the conservation status of pelagophils.
High-resolution airborne magnetic and gravity gradiometry data provide the geophysical framework for evaluating the exploration potential of hidden iron oxide deposits in Mesoproterozoic basement rocks of southeast Missouri. The data are used to calculate mineral prospectivity for iron oxide-apatite (IOA) ± rare earth element (REE) and iron oxide-copper-gold (IOCG) deposits. Results delineate the geophysical footprints of all known iron oxide deposits and reveal several previously unrecognized prospective areas. The airborne data are also inverted to three-dimensional density and magnetic susceptibility models over four concealed deposits at Pea Ridge (IOA ± REE), Boss (IOCG), Kratz Spring (IOA), and Bourbon (IOCG). The Pea Ridge susceptibility model shows a magnetic source that is vertically extensive and traceable to a depth of greater than 2 km. A smaller density source, located within the shallow Precambrian basement, is partly coincident with the magnetic source at Pea Ridge. In contrast, the Boss models show a large (625-m-wide), vertically extensive, and coincident dense and magnetic stock with shallower adjacent lobes that extend more than 2,600 m across the shallow Precambrian paleosurface. The Kratz Spring deposit appears to be a smaller volume of iron oxides and is characterized by lower density and less magnetic rock compared to the other iron deposits. A prospective area identified south of the Kratz Spring deposit shows the largest volume of coincident dense and nonmagnetic rock in the subsurface, and is interpreted as prospective for a hematite-dominant lithology that extends from the top of the Precambrian to depths exceeding 2 km. The Bourbon deposit displays a large bowl-shaped volume of coincident high density and high-magnetic susceptibility rock, and a geometry that suggests the iron mineralization is vertically restricted to the upper parts of the Precambrian basement. In order to underpin the evaluation of the prospectivity and three-dimensional models, an extensive statistical summary of density and apparent magnetic susceptibility measurements is presented that includes data on several hundred samples taken from the deposits, altered wall rocks, and unaltered country rocks.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.111.8.1859","usgsCitation":"McCafferty, A.E., Phillips, J., and Driscoll, R.L., 2016, Magnetic and gravity gradiometry framework for Mesoproterozoic iron oxide-apatite and iron oxide-copper-gold deposits, southeast Missouri, USA: Economic Geology, v. 111, no. 8, https://doi.org/10.2113/econgeo.111.8.1859.","productDescription":"24 p.","startPage":"1882","ipdsId":"IP-069306","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":438504,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78P5XM4","text":"USGS data release","linkHelpText":"Helicopter magnetic and gravity gradiometry survey over the Pea Ridge iron mine and surrounding area, southeast Missouri, 2014"},{"id":331203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.65869140625,\n 35.88905007936091\n ],\n [\n -92.65869140625,\n 38.788345355085625\n ],\n [\n -89.05517578125,\n 38.788345355085625\n ],\n [\n -89.05517578125,\n 35.88905007936091\n ],\n [\n -92.65869140625,\n 35.88905007936091\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"111","issue":"8","edition":"1859","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"58356728e4b0070c0abfb6d2","contributors":{"authors":[{"text":"McCafferty, Anne E. 0000-0001-5574-9201 anne@usgs.gov","orcid":"https://orcid.org/0000-0001-5574-9201","contributorId":1120,"corporation":false,"usgs":true,"family":"McCafferty","given":"Anne","email":"anne@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":654215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Jeffrey 0000-0002-6459-2821 jeff@usgs.gov","orcid":"https://orcid.org/0000-0002-6459-2821","contributorId":127453,"corporation":false,"usgs":true,"family":"Phillips","given":"Jeffrey","email":"jeff@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":654216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Driscoll, Rhonda L. 0000-0001-7725-8956 rdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0001-7725-8956","contributorId":745,"corporation":false,"usgs":true,"family":"Driscoll","given":"Rhonda","email":"rdriscoll@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":654217,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188572,"text":"70188572 - 2016 - Constraining the relative importance of raindrop- and flow-driven sediment transport mechanisms in postwildfire environments and implications for recovery time scales","interactions":[],"lastModifiedDate":"2017-06-16T09:30:12","indexId":"70188572","displayToPublicDate":"2016-11-22T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2312,"text":"Journal of Geophysical Research","active":true,"publicationSubtype":{"id":10}},"title":"Constraining the relative importance of raindrop- and flow-driven sediment transport mechanisms in postwildfire environments and implications for recovery time scales","docAbstract":"Mountain watersheds recently burned by wildfire often experience greater amounts of runoff and increased rates of sediment transport relative to similar unburned areas. Given the sedimentation and debris flow threats caused by increases in erosion, more work is needed to better understand the physical mechanisms responsible for the observed increase in sediment transport in burned environments and the time scale over which a heightened geomorphic response can be expected. In this study, we quantified the relative importance of different hillslope erosion mechanisms during two postwildfire rainstorms at a drainage basin in Southern California by combining terrestrial laser scanner-derived maps of topographic change, field measurements, and numerical modeling of overland flow and sediment transport. Numerous debris flows were initiated by runoff at our study area during a long-duration storm of relatively modest intensity. Despite the presence of a well-developed rill network, numerical model results suggest that the majority of eroded hillslope sediment during this long-duration rainstorm was transported by raindrop-induced sediment transport processes, highlighting the importance of raindrop-driven processes in supplying channels with potential debris flow material. We also used the numerical model to explore relationships between postwildfire storm characteristics, vegetation cover, soil infiltration capacity, and the total volume of eroded sediment from a synthetic hillslope for different end-member erosion regimes. This study adds to our understanding of sediment transport in steep, postwildfire landscapes and shows how data from field monitoring can be combined with numerical modeling of sediment transport to isolate the processes leading to increased erosion in burned areas.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016JF003867","usgsCitation":"McGuire, L., Kean, J.W., Staley, D.M., Rengers, F.K., and Wasklewicz, T.A., 2016, Constraining the relative importance of raindrop- and flow-driven sediment transport mechanisms in postwildfire environments and implications for recovery time scales: Journal of Geophysical Research, v. 121, no. 11, p. 2211-2237, https://doi.org/10.1002/2016JF003867.","productDescription":"27 p.","startPage":"2211","endPage":"2237","ipdsId":"IP-077491","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470407,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jf003867","text":"Publisher Index Page"},{"id":342596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Arroyo Seco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.383333,\n 34.441667\n ],\n [\n -117.875,\n 34.441667\n ],\n [\n -117.875,\n 34.2\n ],\n [\n -118.383333,\n 34.2\n ],\n [\n -118.383333,\n 34.441667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-22","publicationStatus":"PW","scienceBaseUri":"5944ee16e4b062508e333607","contributors":{"authors":[{"text":"McGuire, Luke lmcguire@usgs.gov","contributorId":167018,"corporation":false,"usgs":true,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":698465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wasklewicz, Thad A.","contributorId":39275,"corporation":false,"usgs":true,"family":"Wasklewicz","given":"Thad","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":698397,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178466,"text":"70178466 - 2016 - Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays","interactions":[],"lastModifiedDate":"2018-08-07T12:05:31","indexId":"70178466","displayToPublicDate":"2016-11-21T15:10:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1290,"text":"Comparative Biochemistry and Physiology, Part D: Genomics and Proteomics","active":true,"publicationSubtype":{"id":10}},"title":"Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays","docAbstract":"Environmental studies increasingly identify the presence of both contaminants of emerging concern (CECs) and legacy contaminants in aquatic environments; however, the biological effects of these compounds on resident fishes remain largely unknown. High throughput methodologies were employed to establish partial transcriptomes for three wild-caught, non-model fish species; smallmouth bass (Micropterus dolomieu), white sucker (Catostomus commersonii) and brown bullhead (Ameiurus nebulosus). Sequences from these transcriptome databases were utilized in the development of a custom nCounter CodeSet that allowed for direct multiplexed measurement of 50 transcript abundance endpoints in liver tissue. Sequence information was also utilized in the development of quantitative real-time PCR (qPCR) primers. Cross-species hybridization allowed the smallmouth bass nCounter CodeSet to be used for quantitative transcript abundance analysis of an additional non-model species, largemouth bass (Micropterus salmoides). We validated the nCounter analysis data system with qPCR for a subset of genes and confirmed concordant results. Changes in transcript abundance biomarkers between sexes and seasons were evaluated to provide baseline data on transcript modulation for each species of interest.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cbd.2016.07.001","usgsCitation":"Hahn, C.M., Iwanowicz, L., Cornman, R.S., Mazik, P.M., and Blazer, V., 2016, Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays: Comparative Biochemistry and Physiology, Part D: Genomics and Proteomics, v. 20, p. 27-40, https://doi.org/10.1016/j.cbd.2016.07.001.","productDescription":"14 p.","startPage":"27","endPage":"40","ipdsId":"IP-071925","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":331166,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583415aae4b0070c0abed80e","contributors":{"authors":[{"text":"Hahn, Cassidy M. cmhahn@usgs.gov","contributorId":5321,"corporation":false,"usgs":true,"family":"Hahn","given":"Cassidy","email":"cmhahn@usgs.gov","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":654096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iwanowicz, Luke R. liwanowicz@usgs.gov","contributorId":386,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","email":"liwanowicz@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":654097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":654098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":654099,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":149414,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":654095,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178470,"text":"70178470 - 2016 - Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model","interactions":[],"lastModifiedDate":"2018-09-13T14:45:17","indexId":"70178470","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model","docAbstract":"Reducing uncertainty in data inputs at relevant spatial scales can improve tidal marsh forecasting models, and their usefulness in coastal climate change adaptation decisions. The Marsh Equilibrium Model (MEM), a one-dimensional mechanistic elevation model, incorporates feedbacks of organic and inorganic inputs to project elevations under sea-level rise scenarios. We tested the feasibility of deriving two key MEM inputs—average annual suspended sediment concentration (SSC) and aboveground peak biomass—from remote sensing data in order to apply MEM across a broader geographic region. We analyzed the precision and representativeness (spatial distribution) of these remote sensing inputs to improve understanding of our study region, a brackish tidal marsh in San Francisco Bay, and to test the applicable spatial extent for coastal modeling. We compared biomass and SSC models derived from Landsat 8, DigitalGlobe WorldView-2, and hyperspectral airborne imagery. Landsat 8-derived inputs were evaluated in a MEM sensitivity analysis. Biomass models were comparable although peak biomass from Landsat 8 best matched field-measured values. The Portable Remote Imaging Spectrometer SSC model was most accurate, although a Landsat 8 time series provided annual average SSC estimates. Landsat 8-measured peak biomass values were randomly distributed, and annual average SSC (30 mg/L) was well represented in the main channels (IQR: 29–32 mg/L), illustrating the suitability of these inputs across the model domain. Trend response surface analysis identified significant diversion between field and remote sensing-based model runs at 60 yr due to model sensitivity at the marsh edge (80–140 cm NAVD88), although at 100 yr, elevation forecasts differed less than 10 cm across 97% of the marsh surface (150–200 cm NAVD88). Results demonstrate the utility of Landsat 8 for landscape-scale tidal marsh elevation projections due to its comparable performance with the other sensors, temporal frequency, and cost. Integration of remote sensing data with MEM should advance regional projections of marsh vegetation change by better parameterizing MEM inputs spatially. Improving information for coastal modeling will support planning for ecosystem services, including habitat, carbon storage, and flood protection.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1582","usgsCitation":"Byrd, K.B., Windham-Myers, L., Leeuw, T., Downing, B.D., Morris, J.T., and Ferner, M.C., 2016, Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model: Ecosphere, v. 7, no. 11, e01582; 27 p., https://doi.org/10.1002/ecs2.1582.","productDescription":"e01582; 27 p.","ipdsId":"IP-073438","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470411,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1582","text":"Publisher Index Page"},{"id":438505,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76M34Z1","text":"USGS data release","linkHelpText":"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model"},{"id":331164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335610,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F76M34Z1","text":"Data release for journal article titled, \"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model\""}],"country":"United States","state":"California","otherGeospatial":"Rush Ranch Open Space Preserve, Suisun Slough, Suisun Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.05501556396483,\n 38.17802085110361\n ],\n [\n -122.05501556396483,\n 38.212288054388175\n ],\n [\n -121.99802398681642,\n 38.212288054388175\n ],\n [\n -121.99802398681642,\n 38.17802085110361\n ],\n [\n -122.05501556396483,\n 38.17802085110361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"583415ade4b0070c0abed81a","chorus":{"doi":"10.1002/ecs2.1582","url":"http://dx.doi.org/10.1002/ecs2.1582","publisher":"Wiley-Blackwell","authors":"Byrd Kristin B., Windham-Myers Lisamarie, Leeuw Thomas, Downing Bryan, Morris James T., Ferner Matthew C.","journalName":"Ecosphere","publicationDate":"11/2016","auditedOn":"11/29/2016"},"contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - 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2016 - Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis","interactions":[],"lastModifiedDate":"2017-01-17T19:03:21","indexId":"70178474","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis","docAbstract":"This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained using various remote sensing data and other biogeophysical data, along with 15 flux towers contributing to the grassland model and 15 flux towers for the cropland model. The models yielded weekly mean daily grassland and cropland NEP maps of the U.S. Great Plains at 250 m resolution for 2000–2008. The grassland and cropland NEP maps were spatially summarized and statistically compared. The results of this study indicate that grassland and cropland ecosystems generally performed as weak net carbon (C) sinks, absorbing more C from the atmosphere than they released from 2000 to 2008. Grasslands demonstrated higher carbon sink potential (139 g C·m−2·year−1) than non-irrigated croplands. A closer look into the weekly time series reveals the C fluctuation through time and space for each land cover type.
","language":"English","publisher":"MDPI","doi":"10.3390/rs8110944","usgsCitation":"Wylie, B.K., Howard, D., Dahal, D., Gilmanov, T., Ji, L., Zhang, L., and Smith, K., 2016, Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis: Remote Sensing, v. 8, no. 11, p. 1-28, https://doi.org/10.3390/rs8110944.","productDescription":"Article 944; 28 p.","startPage":"1","endPage":"28","ipdsId":"IP-057161","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":462035,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110944","text":"Publisher Index Page"},{"id":331161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Plains","volume":"8","issue":"11","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"583415ace4b0070c0abed816","contributors":{"authors":[{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howard, Daniel 0000-0002-7563-7538","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":56946,"corporation":false,"usgs":true,"family":"Howard","given":"Daniel","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":654161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":654162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gilmanov, Tagir","contributorId":6351,"corporation":false,"usgs":true,"family":"Gilmanov","given":"Tagir","affiliations":[],"preferred":false,"id":654163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":654164,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Li","contributorId":98139,"corporation":false,"usgs":true,"family":"Zhang","given":"Li","affiliations":[],"preferred":false,"id":654165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Kelcy 0000-0001-6811-1485 kelcy.smith.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":176844,"corporation":false,"usgs":true,"family":"Smith","given":"Kelcy","email":"kelcy.smith.ctr@usgs.gov","affiliations":[],"preferred":false,"id":654166,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178048,"text":"sir20165085 - 2016 - Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012","interactions":[],"lastModifiedDate":"2016-12-19T13:51:19","indexId":"sir20165085","displayToPublicDate":"2016-11-17T09:16:00","publicationYear":"2016","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":"2016-5085","title":"Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012","docAbstract":"In 2012, a late season tropical depression developed into a tropical storm and later a hurricane. The hurricane, named “Hurricane Sandy,” gained strength to a Category 3 storm on October 25, 2012, and underwent several transitions on its approach to the mid-Atlantic region of the eastern coast of the United States. By October 28, 2012, Hurricane Sandy had strengthened into the largest hurricane ever recorded in the North Atlantic and was tracking parallel to the east coast of United States, heading toward New Jersey. On October 29, 2012, the storm turned west-northwest and made landfall near Atlantic City, N.J. The high winds and wind-driven storm surge caused massive damage along the entire coastline of New Jersey. Millions of people were left without power or communication networks. Many homes were completely destroyed. Sand dunes were eroded, and the barrier island at Mantoloking was breached, connecting the ocean with Barnegat Bay.
Several days before the storm made landfall in New Jersey, the U.S. Geological Survey (USGS) made a decision to deploy a temporary network of storm-tide sensors and barometric pressure sensors from Virginia to Maine to supplement the existing USGS and National Oceanic and Atmospheric Administration (NOAA) networks of permanent tide monitoring stations. After the storm made landfall, the USGS conducted a sensor data recovery and high-water-mark collection campaign in cooperation with the Federal Emergency Management Agency (FEMA).
Peak storm-tide elevations documented at USGS tide gages, tidal crest-stage gages, temporary storm sensor locations, and high-water-mark sites indicate the area from southern Monmouth County, N.J., north through Raritan Bay, N.J., had the highest peak storm-tide elevations during this storm. The USGS tide gages at Raritan River at South Amboy and Raritan Bay at Keansburg, part of the New Jersey Tide Telemetry System, each recorded peak storm-tide elevations of greater than 13 feet (ft)—more than 5 ft higher than the previously recorded period-of-record maximum. A comparison of peak storm-tide elevations to preliminary FEMA Coastal Flood Insurance Study flood elevations indicated that these areas experienced the highest recurrence intervals along the coast of New Jersey. Analysis showed peak storm-tide elevations exceeded the 100-year FEMA flood elevations in many parts of Middlesex, Union, Essex, Hudson, and Bergen Counties, and peak storm-tide elevations at many locations in Monmouth County exceeded the 500-year recurrence interval.
A level 1 HAZUS (HAZards United States) analysis was done for the counties in New Jersey affected by flooding to estimate total building stock losses. The aggregated total building stock losses estimated by HAZUS for New Jersey, on the basis of the final inundation verified by USGS high-water marks, was almost $19 billion. A comparison of Hurricane Sandy with historic coastal storms showed that peak storm-tide elevations associated with Hurricane Sandy exceeded most of the previously documented elevations associated with the storms of December 1992, March 1962, September 1960, and September 1944 at many coastal communities in New Jersey. This scientific investigation report was prepared in cooperation with FEMA to document flood processes and flood damages resulting from this storm and to assist in future flood mitigation actions in New Jersey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165085","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Suro, T.P., Deetz, Anna, and Hearn, Paul, 2016, Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012: U.S. Geological Survey Scientific Investigations Report 2016–5085, 73 p., https://dx.doi.org/10.3133/sir20165085.","productDescription":"Report: ix, 73 p.; 5 Tables","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-055579","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":330616,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table4.xls","text":"Table 4","size":"45 KB xls","description":"SIR 2016-5085","linkHelpText":"- Description of U.S. Geological Survey sensors temporarily deployed for Hurricane Sandy with peak storm tide elevations, annual exceedance probabilities, and estimated recurrence intervals in New Jersey, October 29–30, 2012 \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330617,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table5.xls","text":"Table 5","size":"144 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations at 169 high-water-mark sites along the coast of New Jersey during Hurricane Sandy, October 29–30, 2012, and the corresponding Federal Emergency Management Agency flood elevations for the 10-, 50-, 100-, and 500-year recurrence intervals \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330618,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table6.xls","text":"Table 6","size":"61 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations for selected historic coastal floods and peak storm-tide elevations during Hurricane Sandy, October 29–30, 2012, at selected U.S. Geological Survey permanent monitoring tide gages in New Jersey"},{"id":330619,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table7.xls","text":"Table 7","size":"74 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations at 82 high-water-mark sites flagged and surveyed after the December 1992 storm in New Jersey, peak storm-tide elevations from the closest high-water-mark sites flagged and surveyed after Hurricane Sandy, October 29–30, 2012, and peak storm-tide elevations from the nearest U.S. Geological Survey tide gage along the coast of New Jersey during Hurricane Sandy, October 29–30, 2012\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330614,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085.pdf","text":"Report","size":"85.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5085"},{"id":330615,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table3.xls","text":"Table 3","size":"77.5 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak-of-record tide elevations and peak storm-tide elevations at U.S. Geological Survey permanent monitoring tide gages in New Jersey, October 29–30, 2012\t\t\t\t\t\t"},{"id":330613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5085/coverthb.jpg"}],"country":"United States","state":"New 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville NJ, 08648
http://nj.usgs.gov/
Despite widespread and ongoing implementation of conservation practices throughout the Chesapeake Bay watershed, water quality continues to be degraded by excess sediment and nutrient inputs. While the Chesapeake Bay Program has developed and maintains a large-scale and long-term monitoring network to detect improvements in water quality throughout the watershed, fewer resources have been allocated for monitoring smaller watersheds, even though water-quality improvements that may result from the implementation of conservation practices are likely to be first detected at smaller watershed scales.
In 2010, the U.S. Geological Survey partnered with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture to initiate water-quality monitoring in four selected small watersheds that were targeted for increased implementation of conservation practices. Smith Creek watershed is an agricultural watershed in the Shenandoah Valley of Virginia that is dominated by cattle and poultry production, and the Upper Chester River watershed is an agricultural watershed on the Eastern Shore of Maryland that is dominated by row-cropping activities. The Conewago Creek watershed is an agricultural watershed in southeastern Pennsylvania that is characterized by mixed agricultural activities. The fourth watershed, Difficult Run, is a suburban watershed in northern Virginia that is dominated by medium density residential development. The objective of this study was to investigate spatial and temporal variations in water chemistry and suspended sediment in these four relatively small watersheds that represent a range of land-use patterns and underlying geology to (1) characterize current water-quality conditions in these watersheds, and (2) identify the dominant sources, sinks, and transport processes in each watershed.
The general study design involved two components. The first included intensive routine water-quality monitoring at an existing streamgage within each study area (including continuous water-quality monitoring as well as discrete water-quality sampling) to develop a detailed understanding of the temporal and hydrologic variability in stream chemistry and sediment transport in each watershed. The second component involved extensive water-quality monitoring at various sites throughout each watershed to develop a detailed understanding of spatial patterns. Both components were used to improve understanding of sources and transport processes affecting stream chemistry, including nutrients and suspended sediments, and their implications for detecting long-term trends related to best management practices. This report summarizes the results of monitoring that was performed from April 2010 through September 2013.
Summaries for each of the four small watersheds are presented below. Each watershed has a more descriptive and detailed section in the report, but these summaries may be particularly useful for some watershed managers and stakeholders desiring slightly less technical detail.
Smith Creek is a 105.39-mi2 watershed within the Shenandoah Valley that drains to the North Fork Shenandoah River. The long-term Smith Creek base-flow index is 72.3 percent, indicating that on average, approximately 72 percent of Smith Creek flow was base flow, which suggests that Smith Creek streamflow is dominated by groundwater discharge rather than stormwater runoff. A series of cluster and principal components analyses demonstrated that the majority of the variability in Smith Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed for turbidity, suspended sediments, total nitrogen, ammonium, orthophosphate, iron, total phosphorus, and the ratio of calcium to magnesium. Statistically significant inverse correlations with flow were observed for specific conductance, magnesium, δ15N of nitrate, pH, bicarbonate, calcium, and δ18O of nitrate. Of particular note, flow and nitrate were not statistically significantly correlated, likely because of the relatively complex concentration-discharge relationship observed in continuous and discrete datasets. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, turbidity, orthophosphate, total phosphorus, suspended-sediment concentration, and silica were higher during the warm season, but pH, dissolved oxygen, and sulfate were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loads in Smith Creek using the continuous water-quality monitors. The mean Smith Creek in-stream sediment load was approximately 6,900 tons per year, with nearly 90 percent of the sediment load over the 3-year study period contributed during the eight largest storm events during that period. The Smith Creek total phosphorus load was approximately 21,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods, although a substantial phosphorus load still occurs during base-flow conditions. The Smith Creek total nitrogen load was approximately 400,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions (as was the case for sediment and total phosphorus) and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Smith Creek watershed revealed how the complex geology and hydrology interacted to result in variable water chemistry. During relatively dry and low base-flow periods, much of the discharge in Smith Creek was contributed by a single dominant spring—Lacey Spring. During wetter base-flow periods, the flows in Smith Creek were largely generated by a mixture of headwater springs and forested mountain tributaries with very different geochemical composition. The headwater springs generally issued from limestone bedrock and were characterized as having relatively high nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The undeveloped, high-gradient, forested mountain sites were generally characterized by low ionic strength waters with low nutrient concentrations. Nitrate isotope data from the limestone springs generally were consistent with manure-derived nitrogen sources (such as cattle and poultry), although the possibility of other mixed sources cannot be excluded. Nitrate isotope data from the undeveloped, high-gradient forested mountain sites were more consistent with nitrogen from undisturbed soils, atmospheric deposition, or nitrogen fixation. Regardless of the nitrogen source, oxygen isotope data indicate that the nitrate was largely a result of nitrification. Land-use data indicate that manure sources of nitrogen dominated watershed nitrogen inputs. Phosphorus sources were less well studied. The presence of a single point-source discharge near the town of New Market contributed the majority of the phosphorus to Smith Creek under base-flow conditions, but nonpoint sources of phosphorus dominated the loading to Smith Creek during stormflow periods.
Implementation of conservation practices increased in the Smith Creek watershed during the study period, and even though a broad range of practice types was implemented, the most common practices included stream fencing (for cattle exclusion), the development of nutrient management plans, conservation crop rotation, and the planting of cover crops. While the implementation of these conservation practices is encouraging, results indicate small increases in nitrate concentrations at the streamgage over the last 29 years, concurrent with small decreases in nitrate fluxes. It will likely be years before the cumulative effect of these practices can be detected in the Smith Creek water quality, and the magnitude of the effect of these conservation practices detected in Smith Creek will depend largely on whether nutrient loading (of manure and commercial fertilizer) is reduced over time.
The Upper Chester River watershed includes the 36-square-mile (mi2) watershed area around several nontidal tributaries that drain into the tidal Chester River. The streamgage is on Chesterville Branch, the largest nontidal tributary (approximately 6.12 mi2) and is the site for continuous water-quality monitoring for this project. The base-flow index at Chesterville Branch is about 72 percent and indicates that, as in most of the Coastal Plain, groundwater is the greatest contributor to streamflow. As such, more than 90 percent of the nitrogen in the stream is in the form of nitrate from groundwater. Continuous and discrete data collected at Chesterville Branch show the effects of streamflow and season on water quality. Significantly positive correlations with flow were observed for ammonium, dissolved and total phosphorus, sediment, and turbidity as runoff carried these constituents from the land surface into Chesterville Branch. Other constituents that increased significantly with flow include potassium, sulfate, iron, and manganese, which are likely contributed from near-stream areas and ponds with high organic-matter content. Total nitrogen, pH, and specific conductance, along with chemical constituents associated with groundwater inputs including nitrate, calcium, ratio of calcium to magnesium, silica, bicarbonate, and sodium, were negatively correlated with flow because concentrations of these constituents were diluted by runoff.
Seasonal differences in water chemistry, which are most likely related to increased biologic effects on the uptake and release of chemicals in the stream and near-stream areas, also were observed. Water temperature, orthophosphate, δ15N of nitrate, bicarbonate, sodium, and the ratio of sodium to chloride were higher during the warm season, and dissolved oxygen, total nitrogen, nitrate, magnesium, sulfate, and manganese were higher during the cool season.
Surrogate-regression models developed by using continuous water-quality data showed that the annual sediment load for the 2013 water year was about 2,600 tons, with more than 90 percent of this sediment contributed during two storms. The total phosphorus load in 2013 was about 13,000 pounds with more than 90 percent contributed during the same two storms as sediment. The load of total nitrogen, 140,000 pounds, accumulated steadily throughout the 2013 water year as nitrate in groundwater continuously discharged into the stream. The same two large storms that contributed 90 percent of the suspended-sediment and total phosphorus load only contributed about 20 percent of the annual total nitrogen load.
Extensive water-quality monitoring of stream base flow throughout the Upper Chester River watershed identified how differences in land use and hydrogeology affected water chemistry. In parts of the watershed with well-drained soil and thick sandy aquifer sediments, concentrations of nitrate and other chemicals associated with fertilizer and lime application increased in streams as agricultural land use increased. More than 90 percent of the nitrogen in streams from these areas was in the form of nitrate, and concentrations ranged from about 5 milligrams per liter (mg/L) to 8 mg/L as nitrogen in the two largest tributaries. Stream nitrate concentrations were about 1 mg/L as nitrogen where soils were more poorly drained, the surficial aquifer sediments were thinner, and forests and wetlands were more widespread than agriculture. Nitrate isotope data were consistent with inorganic fertilizers ± atmospheric deposition and N2 fixation as sources of nitrogen, and with nitrification as the dominant nitrate-forming process. Nitrate reduction was indicated by elevated δ15N and δ18O values in some samples from streams draining watersheds with poorly drained soils. An analysis of land-use data and SPARROW modeling input data attributed almost 90 percent of the nitrogen sources in the Upper Chester River watershed to inorganic fertilizer and fixation of atmospheric nitrogen by legumes, which is in agreement with the isotopic characteristics of nitrate in this watershed. Local sources of manure are limited in this area. Total phosphorus concentrations during base flow ranged from below detection to about 0.2 mg/L. Stream phosphorus concentrations during base flow were generally lower than those measured during storms because most phosphorus transport likely occurs as phosphorus attached to sediment particles during runoff. Because manure is not widely used in this area, the major source of phosphorus is likely fertilizer.
The implementation of conservation practices in the Upper Chester River watershed increased substantially during the study period, with a total implementation of 1,194 U.S. Department of Agriculture-compliant practices. The most frequently used practices were oriented towards nutrient and sediment control, including cover crops, nutrient management planning, conservation crop rotation, conservation tillage, and irrigation management. The current Chesapeake Bay model for this area predicts that implementation of best management practices should result in a 13-percent decrease in overall delivery of nitrogen to the Upper Chester River. Because most nitrogen travels through the groundwater system for years to decades before being discharged to streams, the time period of monitoring was not sufficient to see the effects of these practices on water quality. The magnitude of the effect that may eventually be detected will depend on the degree to which nitrate leaching into the groundwater system is reduced over time. Loadings of phosphorus and sediment are primarily transported during large runoff events and are difficult to control and analyze for trends because of their timing and episodic nature.
Conewago Creek has two primary monitoring locations—one near the middle of the 47-mi2 watershed and the other near the outlet just upstream of the Susquehanna River. The base-flow index was 47.3 percent for 2012–2013, indicating that on average, approximately 53 percent of the streamflow in Conewago Creek exited the watershed as surface flow, which suggests that the stormwater runoff was somewhat greater than groundwater discharge (base flow). A series of cluster and principal components analyses demonstrated that the majority of the variability in the Conewago Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed at both monitoring sites for ammonium, total phosphorus, orthophosphate, iron, and manganese; additionally, at the upstream monitoring station, total nitrogen demonstrated a statistically significant positive correlation with flow. Statistically significant inverse correlations with flow were observed at both sites for water temperature, specific conductance (at the downstream site only), sulfate, chloride, calcium, and magnesium. Statistically significant seasonal patterns were observed for several water-quality constituents. Water temperature, phosphorus (upstream site only), and orthophosphate were higher during the warm season, and nitrate and total nitrogen (upstream site only) were higher during the cool season.
Surrogate regression models were developed to compute sediment and nutrient load in Conewago Creek by using the continuous water-quality monitors and water-quality samples. Conewago Creek sediment load was approximately 9,900 tons in 2012 and approximately 18,900 tons in 2013, with nearly 80 percent of the sediment load in 2013 contributed by the three largest storm events. Annual total nitrogen loads could not be estimated due to poor model performance. The addition of continued monitoring or a continuously recording nitrate sensor could improve estimates of total nitrogen loads. During 2012 and 2013, phosphorus loads in Conewago Creek were approximately 50,000 pounds in each year.
Combining data from one high-flow synoptic sampling with the data from routine sampling revealed how the geology and hydrology interact to result in variable water chemistry throughout the Conewago Creek watershed. The areas above the upstream gage in the headwaters are generally underlain by forested non-carbonate bedrock and are characterized by relatively low nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The more developed, agricultural areas below the upstream site were generally characterized by higher ionic strength waters with higher nutrient and metal concentrations. An analysis of land-use data and SPAtially Referenced Regressions On Watershed (SPARROW) modeling data indicates that manure sources of nitrogen dominate the input of nitrogen to the watershed.
Implementation of conservation practices increased in the Conewago Creek watershed during the study period, and while a broad range of practice types were implemented, the most common practices included residue and tillage management, cover crops, nutrient management, terracing, and stream fencing (for animal exclusion or bank restoration). While the implementation of these conservation practices is encouraging, the cumulative effects of these practices probably will not be detected in Conewago Creek water quality for several years. The magnitude of the effects of these conservation practices on water quality in Conewago Creek will depend largely on the extent to which nutrient loading (septic, manure, and commercial fertilizer) and sediment-producing activities are reduced over time.
The Difficult Run watershed is a 57.82-mi2 watershed that drains to the Potomac River. The long-term Difficult Run base-flow index (from 1936 to 2010) was 57.9, indicating that approximately 58 percent of streamflow exited the watershed as base flow and 42 percent as stormflow; however, with continued development and urbanization of the watershed, the base-flow index has decreased to 50 percent during the last 20 years. This base-flow index was less than those of the other watersheds evaluated in this study, likely because the Difficult Run watershed largely is underlain by crystalline piedmont metamorphic rocks and has a greater proportion of impervious urban land cover. A series of cluster and principal components analyses indicated that most of the variability in Difficult Run water quality could be attributed to hydrologic variability and seasonality. Statistically significant positive correlations with flow were observed for turbidity, dissolved oxygen, suspended sediments, ammonium, orthophosphate, iron, and total phosphorus. Statistically significant inverse correlations with flow were observed for water temperature, pH, specific conductance, bicarbonate, calcium, magnesium, nitrate, δ15N of nitrate, and silica. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, ammonium, orthophosphate, and δ15N of nitrate were higher during the warm season, and dissolved oxygen, nitrate, and manganese were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loading rates. The Difficult Run sediment load was approximately 8,000 tons per year, with greater than 95 percent of the sediment load in the 2013 water year contributed by the seven largest storm events. The total phosphorus load in Difficult Run was approximately 14,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods. The total nitrogen load in Difficult Run is estimated to have been approximately 140,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions than that of phosphorus and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Difficult Run watershed revealed relatively uniform generation of flow per unit of watershed area, as well as spatial variation in water quality that is strongly related to land-use activities. Elevated nitrate concentrations were observed in a subset of monitoring sites that are inversely correlated with population density and positively correlated to the septic system density within each subwatershed. The majority of the elevated nitrate concentrations for these sites are hypothesized to be caused by nitrate leaching from septic systems, more so than homeowner fertilizer usage among these subwatersheds that have lower population densities than other parts of the watershed. Nitrate isotope data, temporal patterns in the water-quality data, mass-balance computations, and a separate land-use analysis all generally indicate that leachate from septic systems was the likely source of the elevated nitrate. Another group of water-quality sites have relatively low nitrogen concentrations, are located in areas that are served by city sewer lines, and have experienced stream restoration activities. A final group of sites drained the areas with the highest imperviousness and had strongly elevated specific conductance, chloride, and sodium, which were likely caused by a combination of road salting and other anthropogenic sources draining these urbanized areas in the watershed. A fourth group of sites represents a mixture of water sources and had water quality similar to that at the Difficult Run streamgage. Analysis of the nitrate isotope data generally indicates a broad range of composition indicative of mixed natural and anthropogenic nitrogen sources. Implementation of conservation practices increased in the Difficult Run watershed during the study period, and while a broad range of practice types was implemented, the most common practices included stream restoration. While the implementation of these conservation practices is encouraging, the cumulative effect of these practices probably will not be detected in Difficult Run water quality for several years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165093","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program","usgsCitation":"Hyer, K.E., Denver, J.M., Langland, M.J., Webber, J.S., Böhlke, J.K., Hively, W.D., and Clune, J.W., 2016, Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013: U.S. Geological Survey Scientific Investigations Report 2016–5093, 211 p., https://dx.doi.org/10.3133/sir20165093.","productDescription":"Report: xix, 211 p.","startPage":"1","endPage":"211","numberOfPages":"236","onlineOnly":"N","ipdsId":"IP-067371","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":330861,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5093/coverthb.jpg"},{"id":330862,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5093/sir20165093.pdf","text":"Report","size":"30.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5093"}],"country":"United States","state":"Maryland, Pennsylvania, Virginia","otherGeospatial":"Conewago Creek watershed, Difficult Run watershed, Smith Creek watershed, Upper Chester River watershed","geographicExtents":"{\n\"id\": \"2434359\",\n\"crs\": {\n\"type\": \"name\",\n\"properties\": {\n\"name\": \"urn:ogc:def:crs:OGC:1.3:CRS84\"\n}\n},\n\"type\": \"Feature\",\n\"geometry\": {\n\"coordinates\": 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U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
In this study, the U.S. Geological Survey investigated the use of insects as bioindicators of climate change in sagebrush steppe shrublands and grasslands in the Upper Columbia Basin. The research was conducted in the Stinkingwater and Pueblo mountain ranges in eastern Oregon on lands administered by the Bureau of Land Management.
We used a “space-for-time” sampling design that related insect communities to climate and weather along elevation gradients. We analyzed our insect dataset at three levels of organization: (1) whole-community, (2) feeding guilds (detritivores, herbivores, nectarivores, parasites, and predators), and (3) orders within nectarivores (i.e., pollinators). We captured 59,517 insects from 176 families and 10 orders at the Pueblo Mountains study area and 112,305 insects from 185 families and 11 orders at the Stinkingwater Mountains study area in 2012 and 2013. Of all the individuals captured at the Stinkingwater Mountains study area, 77,688 were from the family Cecidomyiidae (Diptera, gall gnats).
We found that the composition of insect communities was associated with variability in long-term (30-yr) temperature and interannual fluctuations in temperature. We found that captures of certain fly, bee, moth, and butterfly pollinators were more strongly associated with some climate and vegetation variables than others. We found that timing of emergence, as measured by first detection of families, was associated with elevation. When analyzed by feeding guilds, we found that all guilds emerged later at high elevations except for detritivores, which emerged earlier at high elevations. The abundance of most taxa varied through time, mostly in response to temperature and precipitation. Of the pollinators, bees (particularly, Halictidae and Megachilidae) peaked in abundance in late June and early July, whereas butterflies and moths peaked in August. Flies peaked in abundance in July.
Overall, our interpretation of these patterns is that insect communities respond positively and negatively to weather and local vegetation more than to long-term climate. Given increasing variability in weather and high probability of extreme weather events, insect communities in sagebrush steppe also may experience considerable fluctuations in composition and abundance, as well as phenology. These findings have implications for many ecosystem services, including pollination, decomposition, and food resources for predatory birds and other vertebrates.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161183","collaboration":"Prepared in cooperation with the Bureau of Land Management under Interagency Agreement L10PG00804 for the project: “Forecasting Insect Community Responses to Changes in Land Management and Climate in Upper Columbia Basin Sagebrush Steppe”","usgsCitation":"Pilliod, D.S., and Rohde, A.T., 2016, Insect community responses to climate and weather across elevation gradients in the Sagebrush Steppe, eastern Oregon: U.S. Geological Open-File Report 2016–1083, 50 p., https://doi.org/10.3133/ofr20161183.","productDescription":"vi, 50 p.","numberOfPages":"61","onlineOnly":"Y","ipdsId":"IP-074398","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":331101,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1183/ofr20161183.pdf","text":"Report","size":"2.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1183"},{"id":331100,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1183/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Sagebrush Steppe","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.33624267578124,\n 43.51668853502906\n ],\n [\n -119.33624267578124,\n 42.70262285884388\n ],\n [\n -118.5809326171875,\n 42.71069600569497\n ],\n [\n -118.13598632812499,\n 42.71473218539458\n ],\n [\n -118.2073974609375,\n 43.69965122967144\n ],\n [\n -118.4600830078125,\n 43.72744458647464\n ],\n [\n -119.33624267578124,\n 43.51668853502906\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
http://fresc.usgs.gov/
The composition of two dune fields, Ogygis Undae and the NE–SW trending dune field in Gale crater (the “Bagnold Dune Field” and “Western Dune Field”), were analyzed using thermal emission spectra from the Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) and the Mars Odyssey Thermal Emission Imaging System (THEMIS). The Gale crater dune field was used as a baseline as other orbital compositional analyses have been conducted, and in situ sampling results will soon be available.
Results from unmixing thermal emission spectra showed a spatial variation between feldspar mineral abundances and pyroxene mineral abundances in Ogygis Undae. Other datasets, including nighttime thermal inertia values, also showed variation throughout the dune field. One explanation proposed for this variation is a bimodal distribution of two sand populations. This distribution is seen in some terrestrial dune fields.
The two dune fields varied in both mineral types present and in uniformity of composition. These differences point to different source lithologies and different distances travelled from source material. Examining these differences further will allow for a greater understanding of aeolian processes on Mars.
","language":"English","publisher":"Elsevier","publisherLocation":"New York, NY","doi":"10.1016/j.epsl.2016.10.022","usgsCitation":"Charles, H., Titus, T.N., Hayward, R., Edwards, C., and Ahrens, C., 2016, Comparison of the mineral composition of the sediment found in two Mars dunefields: Ogygis Undae and Gale crater – three distinct endmembers identified: Earth and Planetary Science Letters, v. 458, no. 15, p. 152-160, https://doi.org/10.1016/j.epsl.2016.10.022.","productDescription":"9 p.","startPage":"152","endPage":"160","ipdsId":"IP-071753","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":331096,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"458","issue":"15","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582ecfece4b04d580bd43528","contributors":{"authors":[{"text":"Charles, Heather hcharles@usgs.gov","contributorId":176924,"corporation":false,"usgs":true,"family":"Charles","given":"Heather","email":"hcharles@usgs.gov","affiliations":[],"preferred":true,"id":653991,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Titus, Timothy N. 0000-0003-0700-4875 ttitus@usgs.gov","orcid":"https://orcid.org/0000-0003-0700-4875","contributorId":146,"corporation":false,"usgs":true,"family":"Titus","given":"Timothy","email":"ttitus@usgs.gov","middleInitial":"N.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":653992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayward, Rosalyn rhayward@usgs.gov","contributorId":176925,"corporation":false,"usgs":true,"family":"Hayward","given":"Rosalyn","email":"rhayward@usgs.gov","affiliations":[],"preferred":true,"id":653994,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edwards, Christopher cedwards@usgs.gov","contributorId":147768,"corporation":false,"usgs":true,"family":"Edwards","given":"Christopher","email":"cedwards@usgs.gov","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":653993,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ahrens, Caitlin","contributorId":176926,"corporation":false,"usgs":false,"family":"Ahrens","given":"Caitlin","email":"","affiliations":[],"preferred":false,"id":653995,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178142,"text":"ofr20161191 - 2016 - GIS-based identification of areas that have resource potential for critical minerals in six selected groups of deposit types in Alaska","interactions":[],"lastModifiedDate":"2023-10-11T01:22:47.377543","indexId":"ofr20161191","displayToPublicDate":"2016-11-16T17:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-1191","title":"GIS-based identification of areas that have resource potential for critical minerals in six selected groups of deposit types in Alaska","docAbstract":"Alaska has considerable potential for undiscovered mineral resources. This report evaluates potential for undiscovered critical minerals in Alaska. Critical minerals are those for which the United States imports more than half of its total supply and which are largely derived from nations that cannot be considered reliable trading partners. In this report, estimated resource potential and certainty for the state of Alaska are analyzed and mapped for the following six selected mineral deposit groups that may contain one or more critical minerals: (1) rare earth elements-thorium-yttrium-niobium(-uranium-zirconium) [REE-Th-Y-Nb(-U-Zr)] deposits associated with peralkaline to carbonatitic igneous intrusive rocks; (2) placer and paleoplacer gold (Au) deposits that in some places might also produce platinum group elements (PGE), chromium (Cr), tin (Sn), tungsten (W), silver (Ag), or titanium (Ti); (3) platinum group elements(-cobalt-chromium-nickel-titanium-vanadium) [PGE(-Co-Cr-Ni-Ti-V)] deposits associated with mafic to ultramafic intrusive rocks; (4) carbonate-hosted copper(-cobalt-silver-germanium-gallium) [Cu(-Co-Ag-Ge-Ga)] deposits; (5) sandstone-hosted uranium(-vanadium-copper) [U(-V-Cu)] deposits; and (6) tin-tungsten-molybdenum(-tantalum-indium-fluorspar) [Sn-W-Mo(-Ta-In-fluorspar)] deposits associated with specialized granites.
This study used a data-driven, geographic information system (GIS)-implemented method to identify areas that have mineral resource potential in Alaska. This method systematically and simultaneously analyzes geoscience data from multiple geospatially referenced datasets and uses individual subwatersheds (12-digit hydrologic units) as the spatial unit of classification. The final map output uses a red, yellow, green, and gray color scheme to portray estimated relative potential (High, Medium, Low, Unknown) for each of the six groups of mineral deposit types, and it indicates the relative certainty (High, Medium, Low) of that estimate for each 12-digit hydrologic unit through color shading. Accompanying tables describe the data layers employed to score favorability for the presence of each mineral deposit group, the values assigned for specific analysis parameters, and the relative weighting of each data layer that contributes to estimated measures of potential and certainty. Core datasets used include the Alaska Geochemical Database, Version 2.0 (AGDB2); the Alaska Division of Geological & Geophysical Surveys (ADGGS) web-based geochemical database; the digital “Geologic Map of Alaska;” the Alaska Resource Data File (ARDF); and aerial gamma-ray surveys flown as part of the National Uranium Resource Evaluation (NURE) Program by the U.S. Department of Energy.
Maps accompanying this report illustrate the scores for estimated mineral resource potential for the six deposit groups for the state of Alaska. Areas that have known potential, as well as new areas that were not previously known to have potential, for the targeted minerals and deposit groups are identified and described. Numerous areas in Alaska, some of them large, have high potential for one or more of the selected groups of deposit types within Alaska.
Matthew Granitto, Timothy S. Hayes, James V. Jones, III, Susan M. Karl, Keith A. Labay, Jeffrey L. Mauk, Jeanine M. Schmidt, Nora B. Shew, Erin Todd, Bronwen Wang, Melanie B. Werdon, and Douglas B. Yager
Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
Mean water depth and range of interannual water-level fluctuations over wet-dry cycles in precipitation are major drivers of vegetation zone formation in North American prairie potholes. We used harmonic hydrological models, which require only mean interannual water depth and amplitude of water-level fluctuations over a wet–dry cycle, to examine how the vegetation zones in a pothole would respond to small changes in water depth and/or amplitude of water-level fluctuations. Field data from wetlands in Saskatchewan, North Dakota, and South Dakota were used to parameterize harmonic models for four pothole classes. Six scenarios in which small negative or positive changes in either mean water depth, amplitude of interannual fluctuations, or both, were modeled to predict if they would affect the number of zones in each wetland class. The results indicated that, in some cases, even small changes in mean water depth when coupled with a small change in amplitude of water-level fluctuations can shift a prairie pothole wetland from one class to another. Our results suggest that climate change could alter the relative proportion of different wetland classes in the prairie pothole region.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0850-8","usgsCitation":"van der Valk, A., and Mushet, D.M., 2016, Interannual water-level fluctuations and the vegetation of prairie potholes: Potential impacts of climate change: Wetlands, v. 36, no. 2, p. 397-406, https://doi.org/10.1007/s13157-016-0850-8.","productDescription":"10 p.","startPage":"397","endPage":"406","ipdsId":"IP-072077","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470416,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1303&context=eeob_ag_pubs","text":"External Repository"},{"id":331061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"582dd8e9e4b04d580bd3fa87","contributors":{"authors":[{"text":"van der Valk, Arnold","contributorId":145612,"corporation":false,"usgs":false,"family":"van der Valk","given":"Arnold","affiliations":[{"id":15296,"text":"Iowa State University, Ames, IA, USA","active":true,"usgs":false}],"preferred":false,"id":653912,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":653911,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}