David A. Johnston Cascades Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Building 10, Suite 100
Vancouver, Washington, 98683-9589
Zebra mussels (Dreissena polymorpha, Pallas, 1771) are an aquatic invasive species in the
United States, and new infestations of zebra mussels can rapidly expand into dense colonies. Zebra
mussels were first reported in Marion Lake, Dakota County, Minnesota, in September 2017, and
surveys indicated the infestation was likely isolated near a public boat access. A 2.4-hectare area
containing the known zebra mussel infestation was enclosed and treated by area resource managers for
9 days with EarthTec QZ (target concentration: 0.5 milligrams per liter as copper), a copper-based
molluscicide, to eradicate the zebra mussels. Researchers led an onsite bioassay to provide an estimate
of the treatment efficacy within the enclosure. The bioassay was conducted in a mobile assay trailer that
received a continuous flow of treated lake water. Bioassay tanks (n=9; 350 liters) within the trailer were
stocked with zebra mussels (25 mussels per containment bag; 7 bags per tank) collected from White
Bear Lake, Ramsey County, Minn. Mortality in the treated bioassay tanks reached a mean of 99 percent
(95-percent confidence interval: 98–100 percent), there were no mortalities in the control tanks.
However, a predictive model produced for timely delivery to area resource managers indicated zebra
mussel mortality within the treated enclosure may have been as low as 85 percent. Onsite bioassays are
a viable and important tool for treatment evaluation particularly in newly infested waterbodies with low
zebra mussel densities.
Director, Upper Midwest Environmental Science Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54602
Mercury (Hg) analyses were conducted on samples of water and sport fish collected from selected sampling sites in the Boise and Snake Rivers and Brownlee Reservoir, in Idaho and Oregon, to meet National Pollution Discharge and Elimination System permit requirements for the City of Boise, Idaho, from 2013 to 2017. City of Boise personnel collected water samples from six sites in October and November of 2013, 2015 and 2017, and sampled one site in 2014 and 2016. Total Hg concentrations in unfiltered water samples ranged from 0.41 to 8.78 nanograms per liter (ng/L), with the highest value (8.78 ng/L) observed in Brownlee Reservoir in 2013. All samples were less than the U.S. Environmental Protection Agency aquatic life criterion of 12 ng/L.
Individual fillets of mountain whitefish (Prosopium williamsoni), rainbow trout (Oncorhynchus mykiss), smallmouth bass (Micropterus dolomieu), and channel catfish (Ictalurus punctatus) were collected and analyzed for Hg. The tissue Hg concentrations were compared with regulatory or advisory values for wet-weight methylmercury in fish tissue. In this report, methylmercury concentrations in fish tissue are considered similar to total Hg in fish muscle tissue and are simply referred to as Hg. The 2013 average Hg concentration for smallmouth bass (0.32 mg/kg) collected at Brownlee Reservoir and for channel catfish (0.33 mg/kg) collected at the Boise River mouth, exceeded the Idaho water quality criterion (>0.3 mg/kg). The 2017 Hg concentrations in smallmouth bass from Brownlee Reservoir (geometric mean of 0.22 mg/kg) was at the Idaho Fish Consumption Advisory Program action level.
Selenium (Se) interacts with Hg to reduce the health risks of Hg, such that tissues with Se-to-Hg molar ratios greater than 1 are considered to present less potential health risks for a given Hg concentration than are tissues with lower Se-to-Hg ratios. One composite fish tissue sample per site was analyzed for Se. Selenium-to-Hg molar ratios in the fish tissue samples ranged from 0.99 to 24.7.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181122","collaboration":"Prepared in cooperation with the City of Boise, Idaho","usgsCitation":"MacCoy, D.E., and Mebane, C.A., 2018, Mercury concentrations in water and mercury and selenium concentrations in fish from Brownlee Reservoir and selected sites in the Boise and Snake Rivers, Idaho and Oregon, 2013-17: U.S. Geological Survey Open-File Report 2018-1122, 37 p., https://doi.org/10.3133/ofr20181122.","productDescription":"iv, 37 p.","onlineOnly":"Y","ipdsId":"IP-091972","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":356923,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1122/coverthb.jpg"},{"id":356924,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1122/ofr20181122.pdf","text":"Report","size":"7.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1122"}],"country":"United States","state":"Idaho, Oregon","otherGeospatial":"Boise River, Brownlee Reservoir, Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.27081298828125,\n 43.241201214257885\n ],\n [\n -116.0540771484375,\n 43.241201214257885\n ],\n [\n -116.0540771484375,\n 44.40827836571936\n ],\n [\n -117.27081298828125,\n 44.40827836571936\n ],\n [\n -117.27081298828125,\n 43.241201214257885\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702
An understanding of riverine water-quality dynamics in regional mixed-land use watersheds is the foundation for advances in landscape biogeochemistry and informed land management. A differential implementation of the statistical/process-based model SPAtially Referenced Regressions on Watershed attributes (SPARROW; Smith et al., https://doi.org/10.1029/97wr02171) is proposed to empirically relate a regional pattern of changes in flow-normalized constituent flux, over a multiyear period, to contemporaneous changes in spatially referenced explanatory variables. In a pilot application, the differential model, called Spatiotemporal Watershed Accumulation of Net effects (SWAN), is used to explore factors influencing changes in flow-normalized flux of total nitrogen over the period 1990–2010 at 43 sites in the nontidal Chesapeake Bay watershed. A seven-parameter model explains 80% of the transformed variability in independently estimated flux changes, indicating that storage effects having characteristic time scales greater than 20 years had a small influence, relative to changes in inputs, on regional water-quality response. Results suggest that 1990–2010 changes in total-nitrogen flux are largely the outcome of increased nonpoint-source pollution associated with urban and suburban development, modulated to the point of negation by terrestrial losses stemming from widespread increases in air temperature and precipitation. The loss mechanism is qualitatively consistent with denitrification; however, increases in aboveground biomass, agricultural nitrogen exports, or hydrologic flushing are also plausible contributors. Although qualified by a small sample size and constraints on explanatory data availability, the pilot suggests that SWAN is a promising approach for broadening scientific understanding of factors driving regional water-quality change and for supporting evidence-based land-management decisions.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2017WR022403","usgsCitation":"Chanat, J.G., and Yang, G., 2018, Exploring drivers of regional water-quality change using differential spatially referenced regression – A pilot study in the Chesapeake Bay watershed: Water Resources Research, v. 54, no. 10, p. 8120-8145, https://doi.org/10.1029/2017WR022403.","productDescription":"26 p.","startPage":"8120","endPage":"8145","ipdsId":"IP-093045","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"links":[{"id":468466,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017wr022403","text":"Publisher Index Page"},{"id":390388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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0000-0001-5587-3683","orcid":"https://orcid.org/0000-0001-5587-3683","contributorId":267279,"corporation":false,"usgs":false,"family":"Yang","given":"Guoxiang","affiliations":[{"id":55459,"text":"NSA Contractor to USGS VA and WV WSC","active":true,"usgs":false}],"preferred":false,"id":824898,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70198358,"text":"ofr20181123 - 2018 - Effects of proposed navigation channel improvements on sediment transport in Mobile Harbor, Alabama","interactions":[],"lastModifiedDate":"2018-08-29T14:58:16","indexId":"ofr20181123","displayToPublicDate":"2018-08-29T08:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1123","title":"Effects of proposed navigation channel improvements on sediment transport in Mobile Harbor, Alabama","docAbstract":"A Delft3D model was developed to evaluate the potential effects of proposed navigation
channel deepening and widening in Mobile Harbor, Alabama. The model performance was
assessed through comparisons of modeled and observed data of water levels, velocities, and bed
level changes; the model captured hydrodynamic and sediment transport patterns in the study
area with skill. The validated model was used to simulate changes in sediment transport for existing
conditions and with the proposed modifications to the navigational channel (with-project),
with and without accounting for 0.5 meter (m) of sea level rise (SLR). Each scenario was simulated
for 1 year with a wave climatology representative of the year 2010 as well as for 10 years
with a longer-term wave climatology spanning from 1988 to 2016. Bed level differences for the
existing and with-project 2010 simulations were minimal, ranging from −0.11 to 0.11 m offshore
of Pelican Island and −0.81 to 0.22 m offshore of the Fort Morgan Peninsula. For the simulations
accounting for 0.5 m of SLR, differences in bed levels from −0.20 to 0.32 m near Pelican
Island and −0.38 to 0.34 m offshore of the Fort Morgan Peninsula. The proposed modifications
reduced the channel shoaling volume by 4.77 and 8.09 percent for the 2010 simulations without
and with 0.5 m of SLR, respectively. For the 10-year simulations, bed level differences for the
existing and with-project simulations ranged from −3.17 to 3.94 m for the simulation without
SLR and −1.92 to 1.47 m for the simulation with 0.5 m of SLR. The with-project condition reduced
the entrance channel shoaling volume by 5.54 percent for the simulation without SLR and
14.98 percent for the simulation with 0.5 m of SLR.
Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Sea-Bird Scientific’s HydroCycle-PO4 phosphate sensor is a single-analyte wet-chemistry sensor designed for in situ environmental monitoring. The unit was evaluated at the U.S. Geological Survey Hydrologic Instrumentation Facility to assess the accuracy of the sensor in solutions with known phosphorous concentration and to test the effects of chromophoric (colored) dissolved organic matter (CDOM) and natural water matrixes on sensor accuracy. Accuracy was tested with three standards: 0.110, 0.174, and 0.260 milligram per liter, as phosphorous (mg/L as P). The 0.110- and 0.260-mg/L standards were made from a dilution of a National Institute of Standards and Technology-traceable phosphate-phosphorous standard with Type I deionized water (DIW). Average measured phosphate concentrations of the tested standards (0.110, 0.174, and 0.260 mg/L as P) in DIW were 0.132, 0.181, and 0.310 mg/L as P, for differences of 20, 4, and 19 percent, respectively.
Measured phosphate concentration of a tested standard was biased by the addition of tea water filtered through a 0.45-micrometer pore size filter (filtered tea water [FTW]) simulating the effect of CDOM. An aliquot of the filtered tea solution was sent to a certified environmental laboratory, which reported a less than reporting level (<0.004 mg/L as P) phosphate concentration. True color of the FTW was measured at 380 platinum-cobalt units by using Standard Methods 8025. For this FTW test, the measured phosphate concentration for the clear 0.260 mg/L as P standard averaged 0.366 mg/L as P. This concentration increased to an average of 0.653 mg/L as P with the addition of 10 percent FTW, and to an average of 0.859 mg/L as P with the addition of 20 percent FTW. These test results indicate a positive bias of up to 40 percent of the concentrations of the measured phosphate concentrations when CDOM is present and indicate a proportional increase in an apparent concentration of phosphorus instrument response as CDOM concentration increases.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181120","usgsCitation":"Snazelle, T.T., 2018, Laboratory evaluation of the Sea-Bird Scientific HydroCycle-PO4 phosphate sensor: U.S. Geological Survey Open-File Report 2018–1120, 10 p., https://doi.org/ofr20181120.","productDescription":"vi, 10 p.","numberOfPages":"20","onlineOnly":"Y","ipdsId":"IP-090916","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":356807,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1120/coverthb5.jpg"},{"id":356711,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1120/ofr20181120.pdf","text":"Report","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1120"}],"contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
Species losses and additions can disrupt the relationship between resident species and the structure and functioning of ecosystems. Persistent human-trampling, on the other hand, can have similar effects through the disruption of biocrusts on surface soils of semiarid systems, affecting soil stability and fixation of carbon and nitrogen. Here, we tested the interactive and synergistic impacts of the exclusion of native mammalian herbivores and the effects of introduced lagomorphs in a semiarid thorn scrub ecosystem, where soils were subjected to two different trampling intensities (i.e., trampled and non-trampled). We postulated that because of their differential habitat use and fossorial activities, with respect to native small mammals, lagomorphs would have strong negative effects on soil structure, biocrust cover, and biocrust bacterial community structure. Our expectations were that changes in biocrust cover in response to trampling where native mammals were excluded, but exotic lagomorphs were present, will spread their impacts on soil chemical and physical features. To test our hypotheses, we measured changes in soil biogeochemical properties in four experimental plots where lagomorphs (L)/small mammals (SM) were experimentally manipulated to exclude them from the plots (−), or let them be present (+). The experimental combinations monitored were: -L/+SM, -L/-SM, +L/+SM, and +L/-SM. Results showed that human-trampling disturbance interacted with the loss of native small mammals and the presence of non-native lagomorphs to cause large changes on biological (i.e., biocrust cover, bacterial and nifH genes abundance), physical (i.e., soil moisture and soil stability) and chemical (i.e., TC and TN) soil features. The relative impacts of trampling disturbance on biological and physicochemical features were strongly influenced by the presence of non-native lagomorphs. For example, larger decreases in biocrust cover and bacterial abundance were observed in treatments without lagomorphs (-L/+SM; -L/-SM). In turn, losses of biocrust cover, in addition to trampling, determined decreases in soil stability in all treatments. These results suggest that non-native lagomorphs surpass the effects of the loss of native small mammals in reducing soil quality and productivity. Therefore, human-trampling has the potential to convert low disturbed soils, as those observed in non-trampled soils in treatments -L/+SM, -L/-SM into poor soils with low biocrusts cover and concomitant low stability, as observed in +L/+SM; +L/-SM treatments. These findings agree with previous observations that different components of global change act in synergic ways in fragile, water-limited environments. Because biological invasions and soil surface disturbance are becoming widespread in dryland regions globally, understanding the long-term consequences of these interactions is essential.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.soilbio.2018.05.019","usgsCitation":"Alfaro, F.D., Manzano, M., Abades, S., Trefault, N., de la Iglesia, R., Gaxiola, A., Marquet, P.A., Gutierrez, J.R., Meserve, P.L., Kelt, D.A., Belnap, J., and Armesto, J.J., 2018, Exclusion of small mammals and lagomorphs invasion interact with human-trampling to drive changes in topsoil microbial community structure and function in semiarid Chile: Soil Biology and Biochemistry, v. 124, p. 1-10, https://doi.org/10.1016/j.soilbio.2018.05.019.","productDescription":"10 p.","startPage":"1","endPage":"10","ipdsId":"IP-098119","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468467,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.soilbio.2018.05.019","text":"Publisher Index Page"},{"id":356844,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Chile","otherGeospatial":"Fray Jorge National Park","volume":"124","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a271e4b0702d0e842ed0","contributors":{"authors":[{"text":"Alfaro, Fernando D.","contributorId":207304,"corporation":false,"usgs":false,"family":"Alfaro","given":"Fernando","email":"","middleInitial":"D.","affiliations":[{"id":37517,"text":"GEMA Center for Genomics, Ecology & Environment, Universidad Mayor, Camino La Piramide 5750, Huechuraba, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":743489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Manzano, Marlene","contributorId":207784,"corporation":false,"usgs":false,"family":"Manzano","given":"Marlene","email":"","affiliations":[{"id":37517,"text":"GEMA Center for Genomics, Ecology & Environment, Universidad Mayor, Camino La Piramide 5750, Huechuraba, Santiago, Chile","active":true,"usgs":false}],"preferred":false,"id":743656,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Abades, Sebastian","contributorId":207377,"corporation":false,"usgs":false,"family":"Abades","given":"Sebastian","affiliations":[],"preferred":false,"id":743657,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Trefault, Nicole","contributorId":207378,"corporation":false,"usgs":false,"family":"Trefault","given":"Nicole","email":"","affiliations":[],"preferred":false,"id":743658,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"de la Iglesia, Rodrigo","contributorId":207379,"corporation":false,"usgs":false,"family":"de la Iglesia","given":"Rodrigo","email":"","affiliations":[],"preferred":false,"id":743659,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gaxiola, Aurora","contributorId":207380,"corporation":false,"usgs":false,"family":"Gaxiola","given":"Aurora","email":"","affiliations":[],"preferred":false,"id":743660,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marquet, Pablo A.","contributorId":176066,"corporation":false,"usgs":false,"family":"Marquet","given":"Pablo","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":743661,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gutierrez, Julio R.","contributorId":207381,"corporation":false,"usgs":false,"family":"Gutierrez","given":"Julio","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":743662,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Meserve, Peter L.","contributorId":207382,"corporation":false,"usgs":false,"family":"Meserve","given":"Peter","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":743663,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kelt, Douglas A.","contributorId":97232,"corporation":false,"usgs":true,"family":"Kelt","given":"Douglas","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":743664,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Belnap, Jayne 0000-0001-7471-2279 jayne_belnap@usgs.gov","orcid":"https://orcid.org/0000-0001-7471-2279","contributorId":1332,"corporation":false,"usgs":true,"family":"Belnap","given":"Jayne","email":"jayne_belnap@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":743665,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Armesto, Juan J.","contributorId":207383,"corporation":false,"usgs":false,"family":"Armesto","given":"Juan","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":743666,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70198555,"text":"sir20185108 - 2018 - Conceptual and numerical models of dissolved solids in the Colorado River, Hoover Dam to Imperial Dam, and Parker Dam to Imperial Dam, Arizona, California, and Nevada","interactions":[],"lastModifiedDate":"2018-08-29T11:01:35","indexId":"sir20185108","displayToPublicDate":"2018-08-28T13:39:02","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5108","title":"Conceptual and numerical models of dissolved solids in the Colorado River, Hoover Dam to Imperial Dam, and Parker Dam to Imperial Dam, Arizona, California, and Nevada","docAbstract":"Conceptual and numerical models were developed to understand and simulate monthly flow-weighted dissolved-solids concentrations in the Colorado River at Imperial Dam. The ability to simulate dissolved-solids concentrations at this location will help the Bureau of Reclamation satisfy the binational agreement on the volume and salinity of Colorado River water delivered to Mexico. A robust spatial- and temporal-resolution dataset that consists of river discharge and dissolved-solids concentration and load information between January 1990 and September 2016 for 10 sites on canals, drains, tributaries, and the main stem of the Colorado River between Hoover and Imperial Dams was generated. Daily mean dissolved-solids concentrations were estimated and monthly mean dissolved-solids loads were computed for each site. Spatial and temporal load patterns, and historical and current controls on loads and concentrations, were analyzed in order to develop a conceptual model of dissolved-solids transport between Hoover and Imperial Dams. Two numerical models describing the relations between dissolved-solids concentrations and components controlling dissolved-solids concentrations and loads were developed, calibrated, and verified.
Between January 1990 and September 2016, there was a 98.8-million-acre-feet loss of water and a 57.0-million-ton loss of dissolved-solids load from the Colorado River between Hoover and Imperial Dams. Between Hoover and Parker Dams, about 69.0 million acre-feet of water was lost and 51.1 million tons of dissolved solids were lost; between Parker and Imperial Dams, about 29.8 million acre-feet of water was lost and 5.9 million tons of dissolved solids were lost. Water was removed from the river at a relatively consistent rate over the 25-year study period through water transfers to California and Arizona, evapotranspiration from crop irrigation, transpiration processes of riparian vegetation, and evaporation from the river main stem. Dissolved solids were removed from the river between Hoover and Parker Dams at a relatively constant rate through water transfers to California and Arizona, and water pumped from the river for irrigation within the Mohave Valley. A small amount of dissolved solids are gained by the river from inflow from the Bill Williams River. Between Parker and Imperial Dams, however, dissolved solids were not removed from the river at a consistent rate over the study period. Dissolved solids were generally removed from the river from 1990 to 2012, then gained by the river from 2012 to 2015, and then removed from the river from 2015 through 2016. Dissolved solids are assumed to be removed from the river and accumulated within the floodplain sediments and aquifers during irrigation processes; some dissolved solids may also be removed from the river through uptake by crops and riparian vegetation. Dissolved solids accumulated on the landscape and in the floodplain aquifer during irrigation are transported to the river during periods when the hydraulic gradient between the floodplain aquifer and the river is increased, causing a gain in dissolved solids in the river. Dissolved-solids gains in the river occur during periods of relatively low river discharge, such as during the winter months and during drier climatic conditions.
Two numerical models were developed and coefficients were estimated by using data from a May 2008-September 2016 calibration period. One model simulates concentrations at Imperial Dam based on the Colorado River system downstream from Parker Dam, and the other model simulates concentrations at Imperial Dam based on the Colorado River system downstream from Hoover Dam. Both models simulated monthly flow-weighted concentrations of dissolved solids for the Colorado River at Imperial Dam, which corresponded well with observed concentrations for the entire study period. The models are more sensitive to input variables of monthly discharge of the Colorado River below Parker Dam and monthly flow-weighted dissolved-solids concentrations of the Colorado River below Hoover Dam and Parker Dam than to the rate of change in concentration with respect to time and the combined discharge of the Colorado River Indian Reservation Main Canal and the Palo Verde Canal. The calibrated models can be used to run scenarios of future monthly flow-weighted dissolved-solids concentrations in the Colorado River at Imperial Dam. Although the models are expected to provide concentration estimates within 18 milligrams per liter (Parker Dam to Imperial Dam model) to 22 milligrams per liter (Hoover Dam to Imperial Dam model), 95 percent of the time, the error of future scenarios increases as uncertainty in the estimated future input variables increases.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Extreme climate events—such as hurricanes, droughts, extreme precipitation, and wildfires—have the potential to alter watershed processes and stream response. Yet due to the destructive and hazardous nature and unpredictability of such events, capturing their hydrochemical signal is challenging. A 5‐year postwildfire study of stream chemistry in the Fourmile Creek watershed, Colorado Front Range, USA, focused on high‐frequency storm sampling. During the study, the watershed was impacted by three additional extreme climate events—drought and two periods of extreme rainfall totals. These events altered concentration‐discharge relationships in ways that elucidate how hydrologic flow paths and source material availability affect stream water chemistry. Reduced infiltration after wildfire led to overland flow during thunderstorms, which conveyed ash and soil into streams. This resulted in elevated stream concentrations of constituents elevated in ash—Ca, K, Mg, alkalinity, and dissolved organic carbon—along with sediment and nitrate. Subsurface flow paths were bypassed, leading to low concentrations of Na and SiO2, which are bedrock derived and not elevated in ash. During drought conditions, when stream discharge was <20% of average, concentrations of sediment, dissolved organic carbon, and Ca fell below average concentrations, but SiO2 did not. Extreme rainfall totals saturated the subsurface and led to prolonged elevated stream discharge. Concentration‐discharge relationships for bedrock‐derived constituents, such as Ca and SiO2, were altered in that time period, while those for dissolved organic carbon were not. Previous disturbances, including historical mining, also affect stream chemistry, and water‐quality impairment can be exacerbated by extreme climate events.
","language":"English","publisher":"AGU","doi":"10.1029/2017JG004349","usgsCitation":"Murphy, S.F., McCleskey, R.B., Martin, D.A., Writer, J., and Ebel, B.A., 2018, Fire, flood, and drought: Extreme climate events alter flow paths and stream chemistry: Journal of Geophysical Research G: Biogeosciences, v. 123, no. 8, p. 2513-2526, https://doi.org/10.1029/2017JG004349.","productDescription":"14 p.","startPage":"2513","endPage":"2526","ipdsId":"IP-080472","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":468468,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2017jg004349","text":"Publisher Index Page"},{"id":356843,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Fourmile Creek Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5833,\n 40\n ],\n [\n -105.33,\n 40\n ],\n [\n -105.33,\n 40.0667\n ],\n [\n -105.5833,\n 40.0667\n ],\n [\n -105.5833,\n 40\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-24","publicationStatus":"PW","scienceBaseUri":"5b98a271e4b0702d0e842ed4","contributors":{"authors":[{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":743505,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCleskey, R. Blaine 0000-0002-2521-8052 rbmccles@usgs.gov","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":147399,"corporation":false,"usgs":true,"family":"McCleskey","given":"R.","email":"rbmccles@usgs.gov","middleInitial":"Blaine","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":743508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Deborah A. 0000-0001-8237-0838 damartin@usgs.gov","orcid":"https://orcid.org/0000-0001-8237-0838","contributorId":168662,"corporation":false,"usgs":true,"family":"Martin","given":"Deborah","email":"damartin@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":743506,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Writer, Jeffrey H.","contributorId":207308,"corporation":false,"usgs":false,"family":"Writer","given":"Jeffrey H.","affiliations":[{"id":29863,"text":"University of Colorado, Boulder, CO","active":true,"usgs":false}],"preferred":false,"id":743507,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ebel, Brian A. 0000-0002-5413-3963 bebel@usgs.gov","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":2557,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian","email":"bebel@usgs.gov","middleInitial":"A.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":743509,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198946,"text":"70198946 - 2018 - Before the storm: Antecedent conditions as regulators of hydrologic and biogeochemical response to extreme climate events","interactions":[],"lastModifiedDate":"2018-12-05T14:19:32","indexId":"70198946","displayToPublicDate":"2018-08-28T13:29:10","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Before the storm: Antecedent conditions as regulators of hydrologic and biogeochemical response to extreme climate events","docAbstract":"While the influence of antecedent conditions on watershed function is widely recognized under typical hydrologic regimes, gaps remain in the context of extreme climate events (ECEs). ECEs are those events that far exceed seasonal norms of intensity, duration, or impact upon the physical environment or ecosystem. In this synthesis, we discuss the role of source availability and hydrologic connectivity on antecedent conditions and propose a conceptual framework to characterize system response to ECEs at the watershed scale. We present four case studies in detail that span a range of types of antecedent conditions and type of ECE to highlight important controls and feedbacks. Because ECEs have the potential to export large amounts of water and materials, their occurrence in sequence can disproportionately amplify the response. In fact, multiple events may not be considered extreme in isolation, but when they occur in close sequence they may lead to extreme responses in terms of both supply and transport capacity. Therefore, to advance our understanding of these complexities, we need continued development of a mechanistic understanding of how antecedent conditions set the stage for ECE response across multiple regions and climates, particularly since monitoring of these rare events is costly and difficult to obtain. Through focused monitoring of critical ecosystems during rare events we will also be able to extend and validate modeling studies. Cross-regional comparisons are also needed to define characteristics of resilient systems. These monitoring, modeling, and synthesis efforts are more critical than ever in light of changing climate regimes, intensification of human modifications of the landscape, and the disproportionate impact of ECEs in highly populated regions.
","language":"English","publisher":"Springer","doi":"10.1007/s10533-018-0482-6","usgsCitation":"McMillan, S.K., Wilson, H.F., Tague, C.L., Hanes, D.M., Inamdar, S., Karwan, D.L., Loecke, T., Morrison, J., Murphy, S.F., and Vidon, P., 2018, Before the storm: Antecedent conditions as regulators of hydrologic and biogeochemical response to extreme climate events: Biogeochemistry, v. 141, no. 3, p. 487-501, https://doi.org/10.1007/s10533-018-0482-6.","productDescription":"15 p.","startPage":"487","endPage":"501","ipdsId":"IP-092162","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":356841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"141","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-16","publicationStatus":"PW","scienceBaseUri":"5b98a271e4b0702d0e842ed6","contributors":{"authors":[{"text":"McMillan, Sara K.","contributorId":207309,"corporation":false,"usgs":false,"family":"McMillan","given":"Sara","email":"","middleInitial":"K.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":743514,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Henry F.","contributorId":207310,"corporation":false,"usgs":false,"family":"Wilson","given":"Henry","email":"","middleInitial":"F.","affiliations":[{"id":24491,"text":"Agriculture and Agri-Food Canada","active":true,"usgs":false}],"preferred":false,"id":743515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tague, Christina L.","contributorId":207311,"corporation":false,"usgs":false,"family":"Tague","given":"Christina","email":"","middleInitial":"L.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":743516,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hanes, Daniel M.","contributorId":207312,"corporation":false,"usgs":false,"family":"Hanes","given":"Daniel","email":"","middleInitial":"M.","affiliations":[{"id":37518,"text":"St. Louis University","active":true,"usgs":false}],"preferred":false,"id":743517,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Inamdar, Shreeram","contributorId":177337,"corporation":false,"usgs":false,"family":"Inamdar","given":"Shreeram","affiliations":[],"preferred":false,"id":743518,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Karwan, Diana L.","contributorId":207315,"corporation":false,"usgs":false,"family":"Karwan","given":"Diana","email":"","middleInitial":"L.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":743522,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Loecke, Terry","contributorId":207313,"corporation":false,"usgs":false,"family":"Loecke","given":"Terry","email":"","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":743519,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Morrison, Jonathan 0000-0002-1756-4609","orcid":"https://orcid.org/0000-0002-1756-4609","contributorId":203255,"corporation":false,"usgs":true,"family":"Morrison","given":"Jonathan","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":743520,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":743513,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vidon, Philippe","contributorId":207314,"corporation":false,"usgs":false,"family":"Vidon","given":"Philippe","email":"","affiliations":[{"id":37519,"text":"SUNY College of Environmental Science and Forestry","active":true,"usgs":false}],"preferred":false,"id":743521,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70198988,"text":"70198988 - 2018 - Input data processing tools for the integrated hydrologic model GSFLOW","interactions":[],"lastModifiedDate":"2018-08-28T13:25:31","indexId":"70198988","displayToPublicDate":"2018-08-28T13:25:28","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Input data processing tools for the integrated hydrologic model GSFLOW","docAbstract":"Integrated hydrologic modeling (IHM) encompasses a vast number of processes and specifications, variable in time and space, and development of models can be arduous. Model input construction techniques have not been formalized or made easily reproducible. Creating the input files for integrated hydrologic models requires complex GIS processing of raster and vector datasets from various sources. Developing stream network topology that is consistent with the model grid-scale digital elevation model (DEM) is important for robust simulation of surface water and groundwater exchanges. Distribution of meteorological data over the model domain is difficult in complex terrain at the model-grid scale, but is necessary for realistic simulations. As model development requires extensive GIS and computer programming expertise, the use of IHMs has mostly been limited to research groups with available financial, human, and technical resources. Here we present a series of open-source Python scripts that are combined with ESRI ArcGIS to provide a formalized technique for the parameterization and development of inputs for the readily available IHM called GSFLOW. This Python toolkit automates many of the necessary and laborious processes of parameterization, including stream network development, land coverages, and meteorological distribution over the model domain. The final products of the toolkit are PRMS ready Parameter Files, along with several input parameters for a MODFLOW model, including input for the Streamflow Routing Package. A demonstration of the toolkit is provided to illustrate its capabilities.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2018.07.020","usgsCitation":"Gardner, M.A., Morton, C.G., Huntington, J., Niswonger, R., and Henson, W.R., 2018, Input data processing tools for the integrated hydrologic model GSFLOW: Environmental Modelling and Software, v. 109, p. 41-53, https://doi.org/10.1016/j.envsoft.2018.07.020.","productDescription":"13 p.","startPage":"41","endPage":"53","ipdsId":"IP-092325","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":468469,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2018.07.020","text":"Publisher Index Page"},{"id":356840,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"109","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a272e4b0702d0e842ed8","contributors":{"authors":[{"text":"Gardner, Murphy A. 0000-0002-3951-6667","orcid":"https://orcid.org/0000-0002-3951-6667","contributorId":207374,"corporation":false,"usgs":true,"family":"Gardner","given":"Murphy","email":"","middleInitial":"A.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":743645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morton, Charles G.","contributorId":207375,"corporation":false,"usgs":false,"family":"Morton","given":"Charles","email":"","middleInitial":"G.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":743652,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huntington, Justin L.","contributorId":31279,"corporation":false,"usgs":true,"family":"Huntington","given":"Justin L.","affiliations":[],"preferred":false,"id":743653,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Niswonger, Richard G. 0000-0001-6397-2403 rniswon@usgs.gov","orcid":"https://orcid.org/0000-0001-6397-2403","contributorId":2833,"corporation":false,"usgs":true,"family":"Niswonger","given":"Richard G.","email":"rniswon@usgs.gov","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":false,"id":743654,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Henson, Wesley R. 0000-0003-4962-5565 whenson@usgs.gov","orcid":"https://orcid.org/0000-0003-4962-5565","contributorId":384,"corporation":false,"usgs":true,"family":"Henson","given":"Wesley","email":"whenson@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":743655,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198963,"text":"70198963 - 2018 - STEPWAT2: An individual‐based model for exploring the impact of climate and disturbance on dryland plant communities","interactions":[],"lastModifiedDate":"2018-08-28T13:00:10","indexId":"70198963","displayToPublicDate":"2018-08-28T12:59:39","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"STEPWAT2: An individual‐based model for exploring the impact of climate and disturbance on dryland plant communities","docAbstract":"The combination of climate change and altered disturbance regimes is directly and indirectly affecting plant communities by mediating competitive interactions, resulting in shifts in species composition and abundance. Dryland plant communities, defined by low soil water availability and highly variable climatic regimes, are particularly vulnerable to climatic changes that exceed their historical range of variability. Individual‐based simulation models can be important tools to quantify the impacts of climate change, altered disturbance regimes, and their interaction on demographic and community‐level responses because they represent competitive interactions between individuals and individual responses to fluctuating environmental conditions. Here, we introduce STEPWAT2, an individual plant‐based simulation model for exploring the joint influence of climate change and disturbance regimes on dryland ecohydrology and plant community composition. STEPWAT2 utilizes a process‐based soil water model (SOILWAT2) to simulate available soil water in multiple soil layers, which plant individuals compete for based on the temporal matching of water and active root distributions with depth. This representation of resource utilization makes STEPWAT2 particularly useful for understanding how changes in soil moisture and altered disturbance regimes will concurrently impact demographic and community‐level responses in drylands. Our goals are threefold: (1) to describe the core modules and functions within STEPWAT2 (model description), (2) to validate STEPWAT2 model output using field data from big sagebrush plant communities (model validation), and (3) to highlight the usefulness of STEPWAT2 as a modeling framework for examining the impacts of climate change and disturbance regimes on dryland plant communities under future conditions (model application). To address goals 2 and 3, we focus on 15 sites that span the spatial extent of big sagebrush plant communities in the western United States. For goal 3, we quantify how climate change, fire, and grazing can interact to influence plant functional type biomass and composition. We use big sagebrush‐dominated plant communities to demonstrate the functionality of STEPWAT2, as these communities are among the most widespread dryland ecosystems in North America.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2394","usgsCitation":"Palmquist, K.A., Bradford, J.B., Martin, T.E., Schlaepfer, D., and Lauenroth, W.K., 2018, STEPWAT2: An individual‐based model for exploring the impact of climate and disturbance on dryland plant communities: Ecosphere, v. 9, no. 8, p. 1-23, https://doi.org/10.1002/ecs2.2394.","productDescription":"e02394; 23 p.","startPage":"1","endPage":"23","ipdsId":"IP-095177","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468472,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2394","text":"Publisher Index Page"},{"id":356834,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"8","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-08-22","publicationStatus":"PW","scienceBaseUri":"5b98a272e4b0702d0e842ee2","contributors":{"authors":[{"text":"Palmquist, Kyle A.","contributorId":169517,"corporation":false,"usgs":false,"family":"Palmquist","given":"Kyle","email":"","middleInitial":"A.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":743612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":743614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martin, Trace E.","contributorId":138852,"corporation":false,"usgs":false,"family":"Martin","given":"Trace","email":"","middleInitial":"E.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":743615,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schlaepfer, Daniel R.","contributorId":105189,"corporation":false,"usgs":false,"family":"Schlaepfer","given":"Daniel R.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":743616,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":743613,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198966,"text":"70198966 - 2018 - Increasing connectivity between metapopulation ecology and landscape ecology","interactions":[],"lastModifiedDate":"2018-08-28T10:15:45","indexId":"70198966","displayToPublicDate":"2018-08-27T20:41:41","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Increasing connectivity between metapopulation ecology and landscape ecology","docAbstract":"Metapopulation ecology and landscape ecology aim to understand how spatial structure influences ecological processes, yet these disciplines address the problem using fundamentally different modeling approaches. Metapopulation models describe how the spatial distribution of patches affects colonization and extinction, but often do not account for the heterogeneity in the landscape between patches. Models in landscape ecology use detailed descriptions of landscape structure, but often without considering colonization and extinction dynamics. We present a novel spatially explicit modeling framework for narrowing the divide between these disciplines to advance understanding of the effects of landscape structure on metapopulation dynamics. Unlike previous efforts, this framework allows for statistical inference on landscape resistance to colonization using empirical data. We demonstrate the approach using 11 yr of data on a threatened amphibian in a desert ecosystem. Occupancy data for Lithobates chiricahuensis (Chiricahua leopard frog) were collected on the Buenos Aires National Wildlife Refuge (BANWR), Arizona, USA from 2007 to 2017 following a reintroduction in 2003. Results indicated that colonization dynamics were influenced by both patch characteristics and landscape structure. Landscape resistance increased with increasing elevation and distance to the nearest streambed. Colonization rate was also influenced by patch quality, with semi‐permanent and permanent ponds contributing substantially more to the colonization of neighboring ponds relative to intermittent ponds. Ponds that only hold water intermittently also had the highest extinction rate. Our modeling framework can be widely applied to understand metapopulation dynamics in complex landscapes, particularly in systems in which the environment between habitat patches influences the colonization process
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2189","usgsCitation":"Howell, P., Muths, E.L., Hossack, B., Sigafus, B., and Chandler, R., 2018, Increasing connectivity between metapopulation ecology and landscape ecology: Ecology, v. 99, no. 5, p. 1119-1128, https://doi.org/10.1002/ecy.2189.","productDescription":"10 p.","startPage":"1119","endPage":"1128","ipdsId":"IP-073405","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":468474,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.2189","text":"Publisher Index Page"},{"id":356817,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"99","issue":"5","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-25","publicationStatus":"PW","scienceBaseUri":"5b98a273e4b0702d0e842ee4","contributors":{"authors":[{"text":"Howell, Paige E.","contributorId":173495,"corporation":false,"usgs":false,"family":"Howell","given":"Paige E.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":743617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":743618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hossack, Blake 0000-0001-7456-9564 blake_hossack@usgs.gov","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":207343,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake","email":"blake_hossack@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":743619,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sigafus, Brent","contributorId":207344,"corporation":false,"usgs":true,"family":"Sigafus","given":"Brent","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":743620,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chandler, Richard rchandler@usgs.gov","contributorId":2511,"corporation":false,"usgs":true,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","affiliations":[{"id":13266,"text":"Warnell School of Forestry and Natural Resources, The University of Georgia","active":true,"usgs":false}],"preferred":false,"id":743621,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70198932,"text":"70198932 - 2018 - Biocrusts: The living skin of the Earth","interactions":[],"lastModifiedDate":"2018-08-27T16:16:52","indexId":"70198932","displayToPublicDate":"2018-08-27T16:16:48","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3089,"text":"Plant and Soil","active":true,"publicationSubtype":{"id":10}},"title":"Biocrusts: The living skin of the Earth","docAbstract":"Biological soil crusts (biocrusts) form a “living skin” at the soil surface in many low productivity ecosystems around the world including water- and cold-limited environments, and early successional seres (Belnap et al. 2003). They may be composed of any configuration of soil surface-dwelling cyanobacteria, eukaryotic algae, lichens, mosses or liverworts, and support assemblages of decomposers and a faunal food web (Belnap et al. 2003). These soil surface communities have global relevance, as it has been recently estimated that they cover about 12% of the terrestrial surface currently (Rodriguez-Caballero et al. 2018). Biocrust communities are perhaps an ideal subject for the journal Plant and Soil, because they are simultaneously plant-like, due to their dominance by autotrophs, yet biocrusts are also clearly a physical feature of the soil given that component organisms are enmeshed in, adherent to, or otherwise in direct contact with the soil surface. The activity of the organisms is what engineers the well-aggregated thin layer at the soil surface that we recognize as a biocrust (Belnap et al. 2003). The contributions of biocrusts to ecosystem function has fueled much research interest, initially in the observation of biocrusts’ soil aggregating and erosion-resisting nature, and later as a multifunctional, globally-relevant ecosystem element instrumental in: 1. building or otherwise altering soil nutrient stocks through N-fixation (Elbert et al. 2012), dust trapping (Reynolds et al. 2001) and nutrient cycling (Strauss et al. 2012), 2. influencing hydrological properties of soil such as the water balance (Chamizo et al. 2016), and 3. The thermal energy balance of the ecosystem (Coradeau et al. 2016, Rutherford et al. 2017).
","language":"English","publisher":"Springer","doi":"10.1007/s11104-018-3735-1","usgsCitation":"Bowker, M.A., Reed, S.C., Maestre, F.T., and Eldridge, D.J., 2018, Biocrusts: The living skin of the Earth: Plant and Soil, v. 429, no. 1-2, p. 1-7, https://doi.org/10.1007/s11104-018-3735-1.","productDescription":"7 p.","startPage":"1","endPage":"7","ipdsId":"IP-098860","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468475,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11104-018-3735-1","text":"Publisher Index Page"},{"id":356812,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"429","issue":"1-2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-07-03","publicationStatus":"PW","scienceBaseUri":"5b98a273e4b0702d0e842ee8","contributors":{"authors":[{"text":"Bowker, Matthew A.","contributorId":196428,"corporation":false,"usgs":false,"family":"Bowker","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":743475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":743476,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maestre, Fernando T.","contributorId":207297,"corporation":false,"usgs":false,"family":"Maestre","given":"Fernando","email":"","middleInitial":"T.","affiliations":[{"id":37513,"text":"Departamento de Biología y Geología, Física y Química Inorgánica, ESCET, Universidad Rey Juan Carlos, c/ Tulipán s/n, 28933 Móstoles, Spain","active":true,"usgs":false}],"preferred":false,"id":743477,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eldridge, David J. 0000-0002-2191-486X","orcid":"https://orcid.org/0000-0002-2191-486X","contributorId":207298,"corporation":false,"usgs":false,"family":"Eldridge","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":37514,"text":"Center for Ecosystem Science, University of New South Wales, Sydney, NSW 2052, Australia","active":true,"usgs":false}],"preferred":false,"id":743478,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191543,"text":"ds1056 - 2018 - Geochemical data for water, streambed sediment, and fish tissue from the Sierra Nevada Mercury Impairment Project, 2011–12","interactions":[],"lastModifiedDate":"2018-08-27T11:21:51","indexId":"ds1056","displayToPublicDate":"2018-08-24T16:02:51","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1056","title":"Geochemical data for water, streambed sediment, and fish tissue from the Sierra Nevada Mercury Impairment Project, 2011–12","docAbstract":"This report presents geochemical data for surface water, streambed sediment, and fish tissue samples collected during low-flow conditions in 20 to 24 Sierra Nevada streams during 2011 and 2012. The dataset is part of a larger study designed to assess the factors that control mercury concentrations in fish tissue and to develop a model that predicts mercury concentration in the tissue of selected fish species in Sierra Nevada streams. The ranges of total mercury concentration observed in different matrices of water and sediment from 24 locations were as follows: below detection to 0.86 nanograms per liter in filtered water, below detection to 4.06 nanograms per liter in suspended particulates (greater than 0.3 micrometer in diameter), 1.1 to 381 nanograms per gram in bed sediment less than 2 millimeters, and 28.1 to 1,410 nanograms per gram in bed sediment less than 0.063 millimeters. The ratio of monomethyl mercury to total mercury ranged as follows: below detection to 19.2 percent in filtered water, below detection to 51.7 percent in suspended particles (greater than 0.3 micrometer), and below detection to 7.6 percent in streambed sediment less than 2 millimeters. Fish from 3 species collected at 20 locations had the following range in total mercury concentration (all concentrations wet weight): 10 to 292 nanograms per gram in rainbow trout (293 fish, 19 locations), 13 to 386 nanograms per gram in brown trout (33 fish, 10 locations), and 159 nanograms per gram in hardhead (1 fish). Concentrations of selenium in fish (wet weight) ranged from 60 to 420 nanograms per gram in rainbow trout (66 fish, 19 locations) and from 180 to 240 nanograms per gram in brown trout (6 fish, 2 locations).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1056","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Stumpner, E.B., Alpers, C.N., Marvin-DiPasquale, M., Agee, J.L., Kakouros, E., Arias, M.R., Kieu, L.H., Roth, D.A., Slotton, D.G., and Fleck, J.A., 2018, Geochemical data for water, streambed sediment, and fish tissue from the Sierra Nevada Mercury Impairment Project, 2011–12: U.S. Geological Survey Data Series 1056, 133 p., https://doi.org/10.3133/ds1056.","productDescription":"Report: xiv, 133 p.","onlineOnly":"Y","ipdsId":"IP-053615","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":356551,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1056/ds_1056.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Data Series 1056"},{"id":356550,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1056/coverthb.jpg"}],"country":"United States","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 37.5\n ],\n [\n -119.5,\n 37.5\n ],\n [\n -119.5,\n 40\n ],\n [\n -122,\n 40\n ],\n [\n -122,\n 37.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The U.S. Geological Survey Coastal and Marine Geology Program uses three methods to derive a datum-based, mean high water shoreline on open-ocean coasts from light detection and ranging (lidar) elevation surveys. This work compared the shorelines produced by the three methods for two different surveys: one survey with simple beach morphology, and one survey with complex beach morphology. For the survey with simple beach morphology, the three methods gave very similar results. The mean differences were less than 0.1 meter, and the root mean square differences were all less than 1.0 meter. For the survey of a beach with complex morphology, the quality control used in the Profile method and Smoothed Contour/Manual Hybrid method produced cleaner shorelines than the Grid method. Only the Profile method can extrapolate if there is no data around mean high water. The Grid and Profile methods produce a point by point estimate of uncertainty which is needed for some applications. Only the Contour method can be easily transferred to external users.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181121","usgsCitation":"Farris, A.S., Weber, K.M., Doran, K.S., and List, J.H., 2018, Comparing methods used by the U.S. Geological Survey Coastal and Marine Geology Program for deriving shoreline position from lidar data: U.S. Geological Survey Open-File Report 2018–1121, 13 p., https://doi.org/10.3133/ofr20181121.","productDescription":"iv, 13 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-097675","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":356712,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1121/coverthb.jpg"},{"id":356713,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1121/ofr20181121.pdf","text":"Report","size":"977 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1121"}],"contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543
The Winnebago Pool is a chain of four shallow lakes (Lake Poygan, Lake Winneconne, Lake Butte des Morts, and Lake Winnebago) that are fed primarily by the Fox and Wolf Rivers, two large agriculturally dominated rivers in Wisconsin, United States. Because the lakes have received extensive phosphorus inputs from their watershed, they have become highly eutrophic with much phosphorus in the water column as well as trapped in their sediments. Each of the four Winnebago Pool lakes has been included on the Wisconsin Department of Natural Resources impaired waters list because of their high total phosphorus concentrations, water-quality use restrictions, and excess algal growth. The study described in this report is part of a Total Maximum Daily Load investigation to determine what actions are needed to improve the water quality (trophic status) of these lakes and thus be able to be removed from the impaired waters list and restore their designated uses. As part of this study, data were collected to describe the existing water quality of the lakes, detailed phosphorus budgets were developed for each of the lakes to describe the different sources of the phosphorus, and two eutrophication models (BATHTUB and Jensen models) were used to determine how much of the phosphorus being input to the lakes needs to be reduced for the lakes to be removed from the impaired waters list and restore their designated uses.
In-lake water-quality data indicated that each of the lakes had extensive vertical mixing that resulted in their water quality deteriorating throughout summer. Each of the lakes had mean summer total phosphorus concentrations exceeding 0.088 milligram per liter (mg/L), well above the 0.040 mg/L criterion for the lakes. Detailed phosphorus budgets for the lakes indicated that the primary sources of phosphorus were from their tributaries (for the most upstream lake in the Winnebago Pool–Lake Poygan) or from a combination of input from the upstream lakes and phosphorus release from the bottom sediment when only the summer months were considered (for the other three lakes).
Model simulations with the BATHTUB and Jensen models indicated that (1) the lakes should have almost linear response in their total phosphorus concentrations to changes in their phosphorus inputs; (2) phosphorus inputs need to be reduced by about 60 percent to the Upper Pool Lakes and 69–73 percent to Lake Winnebago to reduce their mean summer total phosphorus concentrations to 0.040 mg/L; and (3) if all the anthropogenic phosphorus inputs to the lakes could be eliminated, their best possible mean summer total phosphorus concentrations should decrease to about 0.022–0.028 mg/L in the Upper Pool Lakes and to 0.032–0.033 mg/L in Lake Winnebago. The effects of any reduction in phosphorus loading will take many years (50 to more than 75 years) to be fully realized in lake water quality because of phosphorus release from the lake sediments. The effects of nutrient reductions in the watershed of a chain of lakes, such as the Winnebago Pool, gradually cascades down the chain, which has beneficial and detrimental effects. Any action made in the watershed of upstream lakes to reduce phosphorus inputs should improve the water quality of all downstream lakes; however, the upstream lakes delay the response in the downstream lakes, especially in lakes where internal phosphorus loading is important.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185099","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Robertson, D.M., Siebers, B.J., Diebel, M.W., and Somor, A.J., 2018, Water-quality response to changes in phosphorus loading of the Winnebago Pool Lakes, Wisconsin, with special emphasis on the effects of internal loading in a chain of shallow lakes: U.S. Geological Survey Scientific Investigations Report 2018–5099, 58 p., https://doi.org/10.3133/sir20185099.","productDescription":"Report: ix, 58 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094896","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":356707,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5099/coverthb.jpg"},{"id":356708,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5099/sir20185099.pdf","text":"Report","size":"3.70 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5099"},{"id":356709,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Y8BE4H","text":"USGS data release","description":"USGS data release","linkHelpText":"Eutrophication water-quality models and supporting water-quality and phosphorus load data used to simulate changes in the water quality of the Winnebago Pool Lakes, Wisconsin, in response to change in phosphorus loading"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Winnebago Pool Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90,\n 43.5\n ],\n [\n -88.25,\n 43.5\n ],\n [\n -88.25,\n 45.75\n ],\n [\n -90,\n 45.75\n ],\n [\n -90,\n 43.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, USGS Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
To address the 2008/2010 and Supplemental 2014 National Oceanic and Atmospheric Administration Fisheries Biological Opinion for operation of the Federal Columbia River Power System, the U.S. Army Corps of Engineers (USACE) and the Bureau of Reclamation (Reclamation) developed and began implementation of Caspian tern (Hydroprogne caspia) management plans. This implementation includes redistribution of the Caspian terns in the Columbia River estuary and the mid-Columbia River region to reduce predation on salmonids listed under the Endangered Species Act. Key elements of the plans are (1) reduction of nesting habitat for Caspian terns in the Columbia River estuary and the mid-Columbia River region, and (2) creation or modification of nesting habitat at alternative sites within the Caspian tern breeding range. As part of this effort, USACE and Reclamation developed Caspian tern nesting habitat at the U.S. Fish and Wildlife Service Don Edwards San Francisco Bay National Wildlife Refuge (DENWR), California, prior to the 2015 nesting season. Furthermore, nesting habitat for western snowy plovers (Charadrius alexandrinus nivosus) also was developed to provide separate nesting opportunities in the same managed ponds to reduce potential conflicts with Caspian terns. Specifically, seven recently constructed islands within two managed ponds (Ponds A16 and SF2) of DENWR were modified to provide habitat attractive to nesting Caspian terns (5 islands) and snowy plovers (2 islands). These 7 islands were a subset of 46 islands recently constructed in Ponds A16 and SF2 to provide waterbird nesting habitat as part of the South Bay Salt Pond (SBSP) Restoration Project.
We used social attraction methods (decoys and electronic call systems) to attract Caspian terns and snowy plovers to these seven modified islands, and conducted surveys from March to September of 2015, 2016, and 2017 to evaluate nest numbers, nest density, and productivity. Results from the 2015 nesting season, the first year of the study, indicated that island modifications and social attraction measures were successful in establishing Caspian tern breeding colonies at Ponds A16 and SF2 of DENWR. Prior to 2015, there was no history of Caspian terns nesting in either Pond A16 or Pond SF2. The success of 2015 continued in 2016 and 2017. In 2017, the third and final year of the project, Caspian terns initiated at least 664 nests, fledged at least 239 chicks, and had a breeding success rate of 0.36 fledged chicks per breeding pair. This represents a 171 percent increase in the number of breeding pairs and a 41 percent increase in the number of chicks fledged, but a 48 percent decrease in the fledglings produced per breeding pair in 2017 compared to 2015, the first year the colonies were established. The two new large and growing Caspian tern nesting colonies at Ponds A16 and SF2 demonstrate the effectiveness of social attraction measures in helping to establish tern nesting colonies in San Francisco Bay. Social attraction measures similar to those used in this study, but targeting other colonial species such as Forster’s terns (Sterna forsteri) and American avocets (Recurvirostra americana), may help to establish waterbird breeding colonies at wetlands enhanced as part of the SBSP Restoration Project.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181136","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and the Bureau of Reclamation","usgsCitation":"Hartman, C.A., Ackerman, J.T., Herzog, M.P., Strong, C., Trachtenbarg, D., and Shore, C.A., 2018, Social attraction used to establish Caspian tern (Hydroprogne caspia) nesting colonies on modified islands at the Don Edwards San Francisco Bay National Wildlife Refuge, California—Final report: U.S. Geological Survey Open-File Report 2018-1136, 41 p., https://doi.org/10.3133/ofr20181136.","productDescription":"vi, 41 p.","onlineOnly":"Y","ipdsId":"IP-096017","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":356703,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1136/coverthb.jpg"},{"id":356704,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1136/ofr20181136.pdf","text":"Report","size":"3.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1136"}],"country":"United States","state":"California","otherGeospatial":"Don Edwards San Francisco Bay National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.30667114257812,\n 37.38488959341307\n ],\n [\n -121.87889099121092,\n 37.38488959341307\n ],\n [\n -121.87889099121092,\n 37.637616213035884\n ],\n [\n -122.30667114257812,\n 37.637616213035884\n ],\n [\n -122.30667114257812,\n 37.38488959341307\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
The U.S. Geological Survey (USGS) uses Maintenance of Variance Extension Type 1 (MOVE.1) regression to transfer streamflows measured at long-term continuous-record streamgaging stations to partial-record (PR) streamgaging stations where intermittent base-flow measurements are available. MOVE.1 regression is used widely throughout the hydrologic community to extend historic low flows and low-flow statistics at continuous-record streamgaging stations to streamgaging stations that have access to only a partial record of low flows. The method correlates base-flow measurements at PR streamgaging stations with daily mean streamflows measured at index stations that exhibit similar streamflow characteristics.
Following changes in the computing platform for storing, processing, retrieving, and publishing National Water Information System (NWIS) hydrologic data, legacy Statistical Analysis System (SAS) code developed by the USGS to implement the MOVE.1 regression was no longer suitable for reading and processing NWIS streamflow data. To migrate the MOVE.1 program so that it could continue to read streamflow data using the new hydrologic data platform, the SAS code was re-written in R, an open source programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The work described in this report was performed in a study conducted by USGS in cooperation with the New Jersey Department of Environmental Protection.
During migration from SAS to R, graphical and tabular output generated by the R script was compared to output produced by the legacy SAS code to ensure that equations used to perform the MOVE.1 regression remained the same. An option to perform censored MOVE.1 regression was added to extend the MOVE.1 methodology to cases where one or more measured continuous-record or PR streamgaging station flows are zero valued. In addition to permitting censored regression, the new R script includes an option to perform piecewise MOVE.1 regression when the relation between PR station and index station low flows varies significantly across the range of index station streamflows.
Together with traditional MOVE.1 regression, censored, and piecewise MOVE.1 regression methods implemented by the R script offer less biased estimates than ordinary least squares regression for the annual 7-day 10-year and other low-flow statistics at PR stations for a range of base-flow conditions. The R script is used to implement the MOVE.1 regression methods across a variety of computing platforms.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181089","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Colarullo, S.J., Sullivan, S.L., and McHugh, A.R., 2018, Implementation of MOVE.1, censored MOVE.1, and piecewise MOVE.1 low-flow regressions with applications at partial-record streamgaging stations in New Jersey: U.S. Geological Survey Open-File Report 2018–1089, 20 p., https://doi.org/10.3133/ofr20181089.","productDescription":"v, 20 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-089567","costCenters":[{"id":470,"text":"New Jersey Water Science 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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
The maps and graphs in this summary describe national streamflow conditions for water year 2017 (October 1, 2016, to September 30, 2017) in the context of streamflow ranks relative to the 88-year period of 1930–2017, unless otherwise noted. The illustrations are based on observed data from the U.S. Geological Survey (USGS) National Streamflow Network (U.S. Geological Survey, 2018a). The period of 1930–2017 was used because the number of streamgages before 1930 was too small to provide representative data for computing statistics for most regions of the country.
In the summary, reference is made to the term “runoff,” which is the depth to which a river basin, State, or other geographic area would be covered with water if all the streamflow within the area during a specified period was uniformly distributed on it. The value of runoff quantifies the magnitude of water flowing through the Nation’s rivers and streams in measurement units that can be compared from one area to another. In this summary, runoff for a specified period and geographic area is computed from all streamgages with complete record in the geographic area.
In all the graphics, a rank of 1 indicates the highest annual flow of all years analyzed and 88 indicates the lowest annual flow of all years. Rankings of streamflow are grouped into much below normal, below normal, normal, above normal, and much above normal based on percentiles of flow (less than 10 percent, 10–24 percent, 25–75 percent, 76–90 percent, and greater than 90 percent, respectively; U.S. Geological Survey, 2018b). States or water-resources regions are presented in the text in order of ranking; a highest or lowest rank is not shown when there are ties in the rankings. Some of the data used to produce the maps and graphs are provisional and subject to change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183056","usgsCitation":"Jian, X., Wolock, D.M., Brady, S.J., and Lins, H.F., 2018, Streamflow—Water year 2017: U.S. Geological Survey Fact Sheet 2018–3056, 6 p., https://doi.org/10.3133/fs20183056.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"Y","ipdsId":"IP-098559","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":356612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3056/fs20183056.pdf","text":"Report","size":"622 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2018–3056"},{"id":356611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3056/coverthb.jpg"}],"country":"United States","contact":"U.S. Geological Survey
MS 415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
The availability of cold-water refugia during summertime river-water temperature maximums is important for cold-water fish species including Endangered Species Act listed salmonids since water temperature influences metabolism, growth, and phenology. The U.S. Geological Survey monitored water temperature at 10 sites approximately evenly-spaced along the lower Quinault River on the Olympic Peninsula, Washington, from June 2016 to August 2017 to assess thermal conditions in the lower river. During this 15-month period, there was a near-continuous, 15-minute record at 7 of the sites; complications with thermistors at 3 of the 10 sites limited the temperature dataset to include only summer 2016. In addition, near-streambed and water-surface temperatures were measured along the lower river during a longitudinal survey from August 9 to 12, 2016, during summer baseflow conditions to potentially identify cold or cooler water regions. Measured August water temperatures were warmer than model-predicted August temperatures for the period, 1993–2011. Summertime (July–September) daily minimum temperatures exceeded established salmon habitat threshold temperatures of 16 °C (core summer season) and 17.5 °C (spawning, rearing, and migration periods) for 122 and 65 days, respectively, on average at all monitoring sites with a complete 15-month record that included two summer baseflow periods. Summertime water temperatures at those sites were generally cooler in the downstream direction along the lower Quinault River but became warmer in the downstream direction during the rest of the year, suggesting the river was influenced by diffuse discharge of groundwater with a relatively constant annual temperature. The August longitudinal temperature survey did not detect cold-water refugia (features more than 3 °C cooler than ambient stream water), although it did identify 11 cooler water features (CWF) approximately 100–800 m in length that were 0.1 °C cooler than adjacent upstream or downstream water. The CWFs appeared to correspond to local geomorphic conditions. In August 2017, 10 of the 11 CWFs were field surveyed, and 5 appeared to be influenced by shading from solar radiation by riparian vegetation or steep cliff banks. In addition, field observations suggest that finer scale (that is, less than 10 m) CWFs, specifically individual side pools associated with large, in-channel wood, increased in frequency in the downstream direction along the lower Quinault River. However, this study did not quantify the density or water temperatures associated with these fine-scale features that may serve as cool- or cold-water pockets or patches.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181129","collaboration":"Prepared in cooperation with the Quinault Indian Nation","usgsCitation":"Jaeger, K.L., Curran, C.A., Wulfkuhle, E.J., and Opatz, C.O., 2018, Water temperature in the lower Quinault River, Olympic Peninsula, Washington, June 2016–August 2017: U.S. Geological Survey Open-File Report 2018-1129, 24 p., https://doi.org/10.3133/ofr20181129.","productDescription":"Report: iv, 24 p.; Data Release","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-094010","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":356563,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1129/ofr20181129.pdf","text":"Report","size":"6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 20181129"},{"id":356562,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1129/coverthb.jpg"},{"id":363267,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7C53J2D","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water temperature and depth data for the lower Quinault River during summer baseflow, Washington, August 2016 and 2017"}],"country":"United States","state":"Washington","otherGeospatial":"Lower Quinault RIver, Olympic Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.35,\n 47.55\n ],\n [\n -123.5,\n 47.55\n ],\n [\n -123.5,\n 47.25\n ],\n [\n -124.35,\n 47.25\n ],\n [\n -124.35,\n 47.55\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402