Urbanization substantially alters the landscape in ways that can impact stream hydrology, water chemistry, and the health of aquatic communities. Stormwater best management practices (BMPs) are the primary tools used to mitigate the effects of urban stressors such as increased runoff, decreased baseflow, and increased nutrient and sediment transport. To date, Fairfax County Virginia’s stormwater management program has made substantial investments into the implementation of both structural and nonstructural BMPs aimed at restoring and protecting watersheds. The U.S. Geological Survey (USGS), in cooperation with Fairfax County, Virginia, established a long-term water-resources monitoring program to evaluate the watershed-scale effects of these investments. Monitoring began at 14 stations in 2007 and was expanded to 20 stations in 2013. This report utilized the first 10 years of data collection to (1) assess water quantity and quality, as well as ecological condition; (2) compute annual nutrient and sediment loads; and (3) evaluate trends in streamflow, water quality, and ecological condition. Efforts are underway to link the biotic and abiotic patterns described herein to watershed management practices as well as factors such as land use change, public works infrastructure, and climate.
Hydrologic, chemical, and benthic macroinvertebrate community conditions in the streams monitored were similar to those observed in other studies of urban streams. Multidecadal trends in baseflow indices and runoff ratios at long-term Chesapeake Bay Non-tidal Network streamgages (CB-NTN) indicate a decrease in groundwater recharge and increase in storm runoff as a result of urbanization. Streamflow yields varied spatially with land cover, geology, and soil characteristics, whereas flashiness was positively related to impervious area. Dissolved oxygen typically was lowest in the Coastal Plain and across all Triassic Lowlands streams, and highest in the Piedmont. Dissolved oxygen concentrations generally were above Virginia’s minimum criterion of 4.0 milligrams per liter (mg/L), most violations occurred at Paul Spring Branch in the Coastal Plain during the warmest months of the year owing to increased chemical and biological oxygen demand. Typical pH values of the monitored streams centered on neutrality (pH = 7); however, diurnal fluctuations were most prevalent in the continuous pH data at Flatlick Branch (FLAT; a Triassic Lowlands station), as a result of increased photosynthesis catalyzed by phosphorus-rich geology. Specific conductance (SC) varied spatially owing to geology (highest at Triassic Lowlands stations) and anthropogenic disturbance (watersheds with high impervious land cover). Specific conductance typically was inversely related to streamflow except in winter months following deicing road salt applications, when values increased by several orders of magnitude. A significant increase in SC of about 2 percent per year was observed from the combined trend result of all monitoring stations over the 10-year period. Significant SC increases occurred at nearly all monitoring stations. Increasing trends were observed during winter and nonwinter months, which suggests that salts applied to deice roadways and other impervious surfaces are stored in the environment and released year-round.
Suspended-sediment (SS) concentrations in monthly samples did not vary significantly between most stations, but typically were highest in the spring and lowest in the fall as a result of seasonal differences in streamflow and climate. Suspended-sediment yields ranged from 62 to 1,428 tons per square mile (ton/mi2), with a median of 302 ton/mi2. Annual loads were greatest during the wettest water years (October 1-September 30; 2008, 2011, and 2014), with the greatest interannual variability occurring at Difficult Run above Fox Lake (DIFF) and South Fork Little Difficult Run (SFLIL). Suspended sediment was primarily composed of silts and clays; however, the proportion of sand in suspended sediment was related positively to streamflow. Cross-correlation analyses suggested the dominant sources of SS were streambank erosion and resuspension of in-channel material at DIFF and FLAT; whereas, upland sources and erosion of upper streambanks were more common at Dead Run (DEAD), Long Branch (LONG), and SFLIL.
Median total phosphorus (TP) concentrations ranged from 0.016 to 0.077 mg/L, with a networkwide median of 0.022 mg/L, were highest in the warm season (April-September), and were composed primarily of dissolved phosphorous. Although TP concentrations were relatively low across the network, the highest concentrations were consistently at stations located in the Triassic Lowlands, owing to phosphorous-rich geology, and in the Coastal Plain, owing to the low-phosphorous sorptive capacity of those soils. A significant increase in TP concentration occurred in a few stations, but the combined trend results from all stations demonstrated a significant increase of about 4 percent per year. Networkwide increases were also observed in total dissolved phosphorus, orthophosphate, and total particulate phosphorus. The composition of TP shifted from dissolved to particulate as streamflow increased and for this reason loads primarily were composed of particulate phosphorous. Median annual TP loads were highest at FLAT and DEAD and ranged from 247 to 642 pounds per square mile (lbs/mi2) networkwide. Interannual variability in phosphorous yields was apparent at most stations; the highest loading years were also the wettest years during the study period and coincident with the highest peak annual flows.
Total nitrogen (TN) concentrations typically were low throughout the network with exceptions occurring at stations located in watersheds with a high density of septic infrastructure. Elevated TN concentrations also were observed in some watersheds without a high density of septic systems and may be attributable to geologic and soil properties that limit denitrification as well as other unknown anthropogenic inputs. Total nitrogen typically was dominated by nitrate during baseflows; however, the proportion of particulate nitrogen increased during stormflows. Total nitrogen yields were similar across stations, with medians ranging from about 3,600 to 6,300 lbs/mi2 and were related to annual streamflow volume. Total nitrogen concentrations and flow-normalized concentrations decreased over the 10-year period at 7 stations, with median reductions of about 2.5 percent. Increasing trends were observed at the two stations with the highest median TN concentration (Captain Hickory Run and SFLIL, 3–5 mg/L), both watersheds contain a high density of septic infrastructure. The combined trend results from all stations revealed no trend in TN and a declining trend in nitrate of about 2 percent per year.
Overall, benthic community metrics indicated that streams throughout Fairfax County were initially of poor health; however, many metrics show an improving trend (from poor to fair based on the Fairfax County Index of Biological Integrity [IBI]). Significant increasing trends in IBI occurred at the network-scale and at 4 individual stations; additionally, scores improved by at least 1 qualitative category (for example, poor to fair, fair to good) at 11 of the 14 stations between 2009 (the first year all 14 stations were sampled) and 2017. Changes in all metrics suggest that the biodiversity, function, and condition of streams in Fairfax County are improving, but some of these improvements are driven by increased diversity and percent composition of organisms that are tolerant of the urban environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205061","collaboration":"Prepared in cooperation with Fairfax County, Virginia","usgsCitation":"Porter, A.J., Webber, J.S., Witt, J.W., and Jastram, J.D., 2020, Spatial and temporal patterns in streamflow, water chemistry, and aquatic macroinvertebrates of selected streams in Fairfax County, Virginia, 2007–18: U.S. Geological Survey Scientific Investigations Report 2020–5061, 106 p., https://doi.org/10.3133/sir20205061.","productDescription":"Report: xii, 106 p.; Data Release","numberOfPages":"106","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-113872","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":378258,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95S9RFV","text":"USGS data release","linkHelpText":"Inputs and selected outputs used to assess spatial 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 E. Parham Road
Richmond, VA 23228
Pathogenic fungi are increasingly associated with epidemics in wildlife populations. Snake fungal disease (SFD, also referred to as Ophidiomycosis) is an emerging threat to snakes, taxa that are elusive and difficult to sample. Thus, assessments of the effects of SFD on populations have rarely occurred. We used a field technique to enhance detection, Passive Integrated Transponder (PIT) telemetry, and a multi‐state capture–mark–recapture model to assess SFD effects on short‐term (within‐season) survival, movement, and surface activity of two wild snake species, Regina septemvittata (Queensnake) and Nerodia sipedon (Common Watersnake). We were unable to detect an effect of disease state on short‐term survival for either species. However, we estimated Bayesian posterior probabilities of >0.99 that R. septemvittata with SFD spent more time surface‐active and were less likely to permanently emigrate from the study area. We also estimated probabilities of 0.98 and 0.87 that temporary immigration and temporary emigration rates, respectively, were lower in diseased R. septemvittata. We found evidence of elevated surface activity and lower temporary immigration rates in diseased N. sipedon, with estimated probabilities of 0.89, and found considerably less support for differences in permanent or temporary emigration rates. This study is the first to yield estimates for key demographic and behavioral parameters (survival, emigration, surface activity) of snakes in wild populations afflicted with SFD. Given the increase in surface activity of diseased snakes, future surveys of snake populations could benefit from exploring longer‐term demographic consequences of SFD and recognize that disease prevalence in surface‐active animals may exceed that of the population as a whole.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2251","usgsCitation":"McKenzie, J.M., Price, S.J., Connette, G.M., Bonner, S.J., and Lorch, J.M., 2020, Effects of snake fungal disease on short‐term survival, behavior, and movement in free‐ranging snakes: Ecological Applications, v. 31, no. 2, e02251, https://doi.org/10.1002/eap.2251.","productDescription":"e02251","ipdsId":"IP-123269","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":383707,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-15","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenzie, Jennifer M.","contributorId":212841,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":811193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Price, Steven J. 0000-0002-2388-0579","orcid":"https://orcid.org/0000-0002-2388-0579","contributorId":57738,"corporation":false,"usgs":false,"family":"Price","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":811194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connette, Grant M.","contributorId":212844,"corporation":false,"usgs":false,"family":"Connette","given":"Grant","email":"","middleInitial":"M.","affiliations":[{"id":37784,"text":"Smithsonian Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":811195,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonner, Simon J","contributorId":252946,"corporation":false,"usgs":false,"family":"Bonner","given":"Simon","email":"","middleInitial":"J","affiliations":[{"id":13255,"text":"University of Western Ontario","active":true,"usgs":false}],"preferred":false,"id":811196,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":811197,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215074,"text":"70215074 - 2020 - Focused fluid flow and methane venting along the Queen Charlotte fault, offshore Alaska (USA) and British Columbia (Canada)","interactions":[],"lastModifiedDate":"2020-11-30T16:10:25.445752","indexId":"70215074","displayToPublicDate":"2020-11-02T16:29:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Focused fluid flow and methane venting along the Queen Charlotte fault, offshore Alaska (USA) and British Columbia (Canada)","docAbstract":"Fluid seepage along obliquely deforming plate boundaries can be an important indicator of crustal permeability and influence on fault-zone mechanics and hydrocarbon migration. The ~850-km-long Queen Charlotte fault (QCF) is the dominant structure along the right-lateral transform boundary that separates the Pacific and North American tectonic plates offshore southeastern Alaska (USA) and western British Columbia (Canada). Indications for fluid seepage along the QCF margin include gas bubbles originating from the seafloor and imaged in the water column, chemosynthetic communities, precipitates of authigenic carbonates, mud volcanoes, and changes in the acoustic character of seismic reflection data. Cold seeps sampled in this study preferentially occur along the crests of ridgelines associated with uplift and folding and between submarine canyons that incise the continental slope strata. With carbonate stable carbon isotope (δ13C) values ranging from −46‰ to −3‰, there is evidence of both microbial and thermal degradation of organic matter of continental-margin sediments along the QCF. Both active and dormant venting on ridge crests indicate that the development of anticlines is a key feature along the QCF that facilitates both trapping and focused fluid flow. Geochemical analyses of methane-derived authigenic carbonates are evidence of fluid seepage along the QCF since the Last Glacial Maximum. These cold seeps sustain vibrant chemosynthetic communities such as clams and bacterial mats, providing further evidence of venting of reduced chemical fluids such as methane and sulfide along the QCF.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02269.1","usgsCitation":"Prouty, N.G., Brothers, D.S., Kluesner, J.W., Barrie, J., Andrews, B.D., Lauer, R., Greene, G., Conrad, J.E., Lorenson, T., Law, M.D., Sahy, D., Conway, K., McGann, M., and Dartnell, P., 2020, Focused fluid flow and methane venting along the Queen Charlotte fault, offshore Alaska (USA) and British Columbia (Canada): Geosphere, v. 16, no. 6, p. 1336-1357, https://doi.org/10.1130/GES02269.1.","productDescription":"22 p.","startPage":"1336","endPage":"1357","ipdsId":"IP-111343","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02269.1","text":"Publisher Index Page"},{"id":380375,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia","otherGeospatial":"Queen Charlotte Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -138.9111328125,\n 51.83577752045248\n ],\n [\n -129.5068359375,\n 51.83577752045248\n ],\n [\n -129.5068359375,\n 58.07787626787517\n ],\n [\n -138.9111328125,\n 58.07787626787517\n ],\n [\n -138.9111328125,\n 51.83577752045248\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":800716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":201261,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barrie, J. Vaughn","contributorId":242728,"corporation":false,"usgs":false,"family":"Barrie","given":"J. Vaughn","affiliations":[{"id":48497,"text":"2Geological Survey of Canada (Pacific,)","active":true,"usgs":false}],"preferred":false,"id":800717,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrews, Brian D. 0000-0003-1024-9400 bandrews@usgs.gov","orcid":"https://orcid.org/0000-0003-1024-9400","contributorId":201662,"corporation":false,"usgs":true,"family":"Andrews","given":"Brian","email":"bandrews@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800718,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lauer, Rachel","contributorId":242729,"corporation":false,"usgs":false,"family":"Lauer","given":"Rachel","affiliations":[{"id":39897,"text":"Department of Geoscience, University of Calgary","active":true,"usgs":false}],"preferred":false,"id":800719,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Greene, Gary","contributorId":242730,"corporation":false,"usgs":false,"family":"Greene","given":"Gary","affiliations":[{"id":48498,"text":"Moss Landing Marine Laboratories and Tombolo Mapping Laboratory","active":true,"usgs":false}],"preferred":false,"id":800720,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Conrad, James E. 0000-0001-6655-694X jconrad@usgs.gov","orcid":"https://orcid.org/0000-0001-6655-694X","contributorId":2316,"corporation":false,"usgs":true,"family":"Conrad","given":"James","email":"jconrad@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800722,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lorenson, Thomas 0000-0001-7669-2873 tlorenson@usgs.gov","orcid":"https://orcid.org/0000-0001-7669-2873","contributorId":174599,"corporation":false,"usgs":true,"family":"Lorenson","given":"Thomas","email":"tlorenson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800723,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Law, Michael D.","contributorId":218726,"corporation":false,"usgs":false,"family":"Law","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":39897,"text":"Department of Geoscience, University of Calgary","active":true,"usgs":false}],"preferred":false,"id":800724,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sahy, Diana","contributorId":169649,"corporation":false,"usgs":false,"family":"Sahy","given":"Diana","email":"","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":804535,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Conway, Kim","contributorId":242731,"corporation":false,"usgs":false,"family":"Conway","given":"Kim","email":"","affiliations":[{"id":48501,"text":"Geological Survey of Canada (Pacific)","active":true,"usgs":false}],"preferred":false,"id":800725,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":800726,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Dartnell, Peter 0000-0002-9554-729X","orcid":"https://orcid.org/0000-0002-9554-729X","contributorId":208208,"corporation":false,"usgs":true,"family":"Dartnell","given":"Peter","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800727,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70215694,"text":"sir20205086 - 2020 - Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas","interactions":[],"lastModifiedDate":"2020-11-03T12:40:42.087231","indexId":"sir20205086","displayToPublicDate":"2020-11-02T14:07:21","publicationYear":"2020","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":"2020-5086","displayTitle":"Regional Regression Equations for Estimation of Four Hydraulic Properties of Streams at Approximate Bankfull Conditions for Different Ecoregions in Texas","title":"Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas","docAbstract":"The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, assessed statistical relations between hydraulic properties of streams at approximate bankfull conditions for different ecological regions (ecoregions) in Texas. Data from more than 103,000 records of measured discharge and ancillary hydraulic properties were assembled from summaries of discharge measurements for 424 U.S. Geological Survey streamgages in Texas. The data were subsequently subsetted at each streamgage for a streamgage-specific discharge interval centered on the estimated median annual peak discharge (0.5 annual exceedance probability) obtained from previously published regional regression equations in Texas in conjunction with the streamgage-specific sample median annual peak discharge for the period of record for each streamgage. Discharge measurements at gaged locations representing bankfull conditions (approximated from a discharge interval centered on the estimated median annual peak discharge at a given site) and associated watershed properties were subjected to rigorous statistical analysis. For most discharge measurements (where discharge is symbolically represented as Q), the following hydraulic properties are available: cross-section area (A), water-surface top width (B), and reported mean velocity (V). Statewide summary statistics were computed by using these four hydraulic properties (Q, A, B, and V) and the following five watershed properties: (1) watershed area (contributing drainage area), (2) a multiple of main-channel slope (1,000 times main-channel slope), (3) mean annual precipitation, (4) drainage density, and (5) sinuosity ratio. From the initial set of 424 streamgages, summary statistics were computed for 372 selected streamgages in Texas and constitute the subsetted measurements dataset described in this report. Eight of the 10 ecoregions in Texas are represented in the statewide summary statistics.
The resulting statistical relations, expressed as regression equations, can be used to estimate cross-section area, water-surface top width, discharge, and mean velocity of streams in different Texas ecoregions, at approximate bankfull conditions. In the regression equations, watershed properties were the independent variables for applicable watersheds, and predictions from the equations might be useful for estimating the four hydraulic properties at ungaged or unmonitored locations from selected characteristics measured at both the ungaged locations and gaged locations.
Four regression equations to estimate the four hydraulic properties were identified as the preferred equations from this study. The four preferred equations use watershed area, mean annual precipitation, and aggregated ecoregion (treated as a categorical variable) to estimate the hydraulic properties, and justification is provided for this preference. For the four equations, the proportions of variance explained by the regression equations as measured by Nash-Sutcliffe efficiency are about 71 percent for cross-section area, 36 percent for top width, 76 percent for discharge, and 25 percent for mean velocity. Residual standard error (RSEs) of the regression equations are 0.252 log10 square feet for cross-section area, 0.319 log10 feet for top width, 0.247 log10 cubic feet per second for discharge, and 0.190 log10 feet per second for mean velocity, and the corresponding standard deviations of response are 0.465 log10 square feet, 0.397 log10 feet, 0.507 log10 cubic feet per second, and 0.220 log10 feet per second, respectively. The residual standard errors are less than the standard deviations as anticipated but show that the uncertainty reduction (percent change) for cross-section area is about −46 percent, about −20 percent for top width, about −51 percent for discharge, and about −14 percent for mean velocity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205086","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Asquith, W.H., Gordon, J.D., and Wallace, D.S., 2020, Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas: U.S. Geological Survey Scientific Investigations Report 2020–5086, 45 p., https://doi.org/10.3133/sir20205086.","productDescription":"Report: vi, 45 p.; Companion File","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-081456","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":436735,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96WY938","text":"USGS data release","linkHelpText":"Source code in R for creation of regional regression equations for estimation of four 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\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Ice wedge degradation is a widespread occurrence across the circumpolar Arctic causing extreme spatial heterogeneity in water distribution, vegetation, and energy balance across landscapes. These heterogeneities influence carbon dioxide (CO2) and methane (CH4) fluxes, yet there is little understanding of how they effect change in landscape‐level carbon (C) gas flux over time. We measured CO2 and CH4 fluxes in an area undergoing ice wedge degradation near Prudhoe Bay, Alaska, and combined with repeat imagery analysis to estimate seasonal landscape‐level C flux response to geomorphic change. Net CO2 and CH4 emissions changed by −25% and +42%, respectively, resulting in a 14% increase in seasonal CO2‐C equivalent emissions over 69 years as ice wedge degradation formed water‐filled troughs. The dynamic ice wedge degradation/stabilization process can cause significant changes in CO2 and CH4 fluxes over time, and the integration of this process is important to forecasting landscape‐level C fluxes in permafrost regions abundant in ice wedges.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL089894","usgsCitation":"Wickland, K.P., Jorgenson, M., Koch, J.C., Kanevskiy, M.Z., and Striegl, R.G., 2020, Carbon dioxide and methane flux in a dynamic Arctic tundra landscape: Decadal‐scale impacts of ice wedge degradation and stabilization: Geophysical Research Letters, v. 47, no. 22, e2020GL089894, 10 p., https://doi.org/10.1029/2020GL089894.","productDescription":"e2020GL089894, 10 p.","ipdsId":"IP-120601","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":380783,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prudhoe Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.73428344726562,\n 70.00180966478055\n ],\n [\n -147.8704833984375,\n 70.00180966478055\n ],\n [\n -147.8704833984375,\n 70.35709062721314\n ],\n [\n -148.73428344726562,\n 70.35709062721314\n ],\n [\n -148.73428344726562,\n 70.00180966478055\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"22","noUsgsAuthors":false,"publicationDate":"2020-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":805612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgenson, M.Torre 0000-0002-9834-8851","orcid":"https://orcid.org/0000-0002-9834-8851","contributorId":245200,"corporation":false,"usgs":false,"family":"Jorgenson","given":"M.Torre","affiliations":[{"id":13506,"text":"Alaska Ecoscience","active":true,"usgs":false}],"preferred":false,"id":805613,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":805614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanevskiy, Mikhail Z.","contributorId":199153,"corporation":false,"usgs":false,"family":"Kanevskiy","given":"Mikhail","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":805615,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions 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":false,"id":805616,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216106,"text":"70216106 - 2020 - Development of a submerged aquatic vegetation growth model in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST v3.4) model","interactions":[],"lastModifiedDate":"2020-11-05T14:23:09.49639","indexId":"70216106","displayToPublicDate":"2020-11-02T08:20:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Development of a submerged aquatic vegetation growth model in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST v3.4) model","docAbstract":"The coupled biophysical interactions between submerged aquatic vegetation (SAV), hydrodynamics (currents and waves), sediment dynamics, and nutrient cycling have long been of interest in estuarine environments. Recent observational studies have addressed feedbacks between SAV meadows and their role in modifying current velocity, sedimentation, and nutrient cycling. To represent these dynamic processes in a numerical model, the presence of SAV and its effect on hydrodynamics (currents and waves) and sediment dynamics was incorporated into the open-source Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. In this study, we extend the COAWST modeling framework to account for dynamic changes of SAV and associated epiphyte biomass. Modeled SAV biomass is represented as a function of temperature, light, and nutrient availability. The modeled SAV community exchanges nutrients, detritus, dissolved inorganic carbon, and dissolved oxygen with the water-column biogeochemistry model. The dynamic simulation of SAV biomass allows the plants to both respond to and cause changes in the water column and sediment bed properties, hydrodynamics, and sediment transport (i.e., a two-way feedback). We demonstrate the behavior of these modeled processes through application to an idealized domain and then apply the model to a eutrophic harbor where SAV dieback is a result of anthropogenic nitrate loading and eutrophication. These cases demonstrate an advance in the deterministic modeling of coupled biophysical processes and will further our understanding of future ecosystem change.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/gmd-13-5211-2020","usgsCitation":"Kalra, T., Ganju, N., and Testa, J.M., 2020, Development of a submerged aquatic vegetation growth model in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST v3.4) model: Geoscientific Model Development, v. 13, no. 11, p. 5211-5228, https://doi.org/10.5194/gmd-13-5211-2020.","productDescription":"18 p.","startPage":"5211","endPage":"5228","ipdsId":"IP-102944","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-13-5211-2020","text":"Publisher Index Page"},{"id":380185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":804107,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":804108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Testa, Jeremy M.","contributorId":244524,"corporation":false,"usgs":false,"family":"Testa","given":"Jeremy","email":"","middleInitial":"M.","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":804109,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237973,"text":"70237973 - 2020 - High-frequency data reveal deicing salts drive elevated specific conductance and chloride along with pervasive and frequent exceedances of the U.S. Environmental Protection Agency aquatic life criteria for chloride in urban streams","interactions":[],"lastModifiedDate":"2022-11-02T11:44:45.440534","indexId":"70237973","displayToPublicDate":"2020-11-02T06:43:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"High-frequency data reveal deicing salts drive elevated specific conductance and chloride along with pervasive and frequent exceedances of the U.S. Environmental Protection Agency aquatic life criteria for chloride in urban streams","docAbstract":"Increasing specific conductance (SC) and chloride concentrations [Cl] negatively affect many stream ecosystems. We characterized spatial variability in SC, [Cl], and exceedances of Environmental Protection Agency [Cl] criteria using nearly 30 million high-frequency observations (2–15 min intervals) for SC and modeled [Cl] from 93 sites across three regions in the eastern United States: Southeast, Mid-Atlantic, and New England. SC and [Cl] increase substantially from south to north and within regions with impervious surface cover (ISC). In the Southeast, [Cl] weakly correlates with ISC, no [Cl] exceedances occur, and [Cl] concentrations are constant with time. In the Mid-Atlantic and New England, [Cl] and [Cl] exceedances strongly correlate with ISC. [Cl] criteria are frequently exceeded at sites with greater than 9–10% ISC and median [Cl] higher than 30–80 mg/L. Tens to hundreds of [Cl] exceedances observed annually at most of these sites help explain previous research where stream ecosystems showed changes at (primarily nonwinter) [Cl] as low as 30–40 mg/L. Mid-Atlantic chronic [Cl] exceedances occur primarily in December–March. In New England, exceedances are common in nonwinter months. [Cl] is increasing at nearly all Mid-Atlantic and New England sites with the largest increases at sites with higher [Cl].
Wildfire is a worldwide phenomenon that is expected to increase in extent and severity in the future, due to fuel accumulations, shifting land management practices, and climate change. It immediately affects the landscape by removing vegetation, depositing ash, influencing water-repellent soil formation, and physically weathering boulders and bedrock. These changes typically lead to increased erosion through sheetwash, rilling, dry ravel, and increased mass movement in the form of floods, debris flow, rockfall, and landslides. These process changes bring about landform changes as hillslopes are lowered and stream channels aggrade or incise at increased rates. Furthermore, development of alluvial fans, debris fans, and talus cones are enhanced. The window of disturbance to the landscape caused by wildfire is typically on the order of 3–4 years, with some effects persisting up to 30 years.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference module in earth systems and environmental sciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","doi":"10.1016/B978-0-12-818234-5.00017-1","usgsCitation":"Santi, P., and Rengers, F.K., 2020, Wildfire and landscape change, chap. of Reference module in earth systems and environmental sciences, HTML Document, https://doi.org/10.1016/B978-0-12-818234-5.00017-1.","productDescription":"HTML Document","ipdsId":"IP-119751","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":382559,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Santi, Paul M.","contributorId":247562,"corporation":false,"usgs":false,"family":"Santi","given":"Paul M.","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":807909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":807910,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70217341,"text":"70217341 - 2020 - Ratios of methylmercury to total mercury in predator and primary consumer insects from Adirondack streams in New York State","interactions":[],"lastModifiedDate":"2021-01-18T16:42:24.056408","indexId":"70217341","displayToPublicDate":"2020-11-01T10:35:15","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5792,"text":"Summary Report","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"20-32","title":"Ratios of methylmercury to total mercury in predator and primary consumer insects from Adirondack streams in New York State","docAbstract":"Mercury (Hg) is a global pollutant that affects aquatic biota in otherwise pristine settings such as the Adirondack region of New York State. Bioaccumulation of Hg is especially problematic in sensitive landscapes, where inorganic mercury from atmospheric deposition is readily converted, via natural processes, to methylmercury (MeHg), the toxic form that is taken up and biomagnified in aquatic food webs. There is great interest in monitoring MeHg in aquatic biota across these sensitive regions to evaluate responses to changes in Hg emissions. Aquatic insects, such as dragonfly larvae, have great potential as MeHg “biosentinels,” but currently are not widely used for this purpose. An important practical consideration in the use of aquatic insects for MeHg biomonitoring is whether total mercury (THg) is a suitable surrogate for MeHg, which is much more technically challenging and expensive to analyze than is THg. The objective of this project was to assess the suitability of THg as a surrogate for MeHg in stream-dwelling insects. Specifically, existing data on immature aquatic insects from nine Adirondack streams were used to characterize MeHg to THg ratios (i.e., MeHg%), and variation in these ratios (e.g., among sites, seasons, taxa) in predator and primary consumer insects, examine how well THg in different groups tracks measured stream water MeHg (i.e., filtered MeHg; FMeHg), and explore the influence of trophic position (indicated by nitrogen stable isotopes; δ15N) on the observed MeHg% patterns.
Three broad insect feeding groups were included in this analysis: predators, shredders, and scrapers. Predators had the highest MeHg% (median 94%), and MeHg% did not differ significantly among any of the taxa considered: stoneflies, damselflies, and three families of dragonflies (darners, common skimmers, and clubtails). Darners and common skimmers, the most numerous and abundant predators, were combined for further analyses. Site medians for these “selected dragonflies” were all at least 90% (summer-fall collections) and MeHg% did not differ significantly among sites. The correlation between FMeHg and THg in selected dragonflies was nearly as strong as that of FMeHg and dragonfly MeHg. In contrast, median MeHg% in shredders (northern caddisflies) and scrapers (flathead mayflies), which are both primary consumers, was lower overall (medians 52% and 35%, respectively), more variable, and less-well representative of FMeHg than predators. Stable isotope results indicate that variation in feeding position is an important influence on some of the MeHg% patterns observed in this study. This study’s findings suggest that THg is likely to be a suitable surrogate for MeHg in predatory aquatic insects from Adirondack streams, but do not support the use of THg in primary consumers for regional MeHg monitoring.
","language":"English","publisher":"New York State Energy Research and Development Authority","usgsCitation":"Riva-Murray, K., 2020, Ratios of methylmercury to total mercury in predator and primary consumer insects from Adirondack streams in New York State: Summary Report 20-32, vi, 15 p.","productDescription":"vi, 15 p.","ipdsId":"IP-103615","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":382274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":382272,"type":{"id":15,"text":"Index Page"},"url":"https://www.nyserda.ny.gov/About/Publications/Research-and-Development-Technical-Reports/Environmental-Research-and-Development-Technical-Reports#eco"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.805908203125,\n 44.02442151965934\n ],\n [\n -73.85009765625,\n 44.02442151965934\n ],\n [\n -73.85009765625,\n 44.5435052132082\n ],\n [\n -74.805908203125,\n 44.5435052132082\n ],\n [\n -74.805908203125,\n 44.02442151965934\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Riva-Murray, Karen 0000-0001-6683-2238 krmurray@usgs.gov","orcid":"https://orcid.org/0000-0001-6683-2238","contributorId":2984,"corporation":false,"usgs":true,"family":"Riva-Murray","given":"Karen","email":"krmurray@usgs.gov","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":808421,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70222481,"text":"70222481 - 2020 - Wildfire and Earth surface processes","interactions":[],"lastModifiedDate":"2021-08-02T15:49:34.707051","indexId":"70222481","displayToPublicDate":"2020-11-01T08:31:55","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Wildfire and Earth surface processes","docAbstract":"Wildfire is a worldwide phenomenon that is expected to increase in extent and severity in the future, due to fuel accumulations, shifting land management practices, and climate change. It immediately affects the landscape by removing vegetation, depositing ash, influencing water-repellent soil formation, and physically weathering boulders and bedrock. These changes typically lead to increased erosion through sheetwash, rilling, dry ravel, and increased mass movement in the form of floods, debris flow, rockfall, and landslides. These process changes bring about landform changes as hillslopes are lowered and stream channels aggrade or incise at increased rates. Furthermore, development of alluvial fans, debris fans, and talus cones are enhanced. The window of disturbance to the landscape caused by wildfire is typically on the order of 3–4 years, with some effects persisting up to 30 years.
Executive Summary Under the Resources and Ecosystem Sustainability, Tourist Opportunities, and Revived Economies of the Gulf Coast States Act of 2012 (RESTORE Act), the Gulf Coast Ecosystem Restoration Council (RESTORE Council or Council) is required to report on the progress of funded projects and programs. Systematic monitoring of restoration at the project-specific and programmatic-levels (watershed and Gulf of Mexico) enables consistent reporting and gives the public confidence that the restoration investments selected by the RESTORE Council will be evaluated and adaptively managed accordingly. Monitoring information that has been collected at different spatial and temporal scales can be used as the foundation to illustrate progress towards comprehensive ecosystem restoration goals and objectives that promote holistic Gulf of Mexico recovery (see ‘RESTORE Council Background’ at the beginning of this report for additional Council information).
Currently, federal, state and local agencies, universities, private industry, and non-governmental organizations (NGOs) are conducting monitoring activities at various scales around the Gulf of Mexico. In addition, each RESTORE Council-funded project will, at a minimum, perform project-specific monitoring. This collection of monitoring activities was inventoried and coordinated into a network of existing programs by the Council-funded RESTORE Council Monitoring and Assessment Program (CMAP), which will suggest opportunities for efficiencies and collaborative cross-program review of performance with other Gulf ecosystem recovery efforts. CMAP was designed and funded to inventory and integrate existing monitoring efforts, improve discovery and accessibility of existing monitoring data, and ensure the collected information supports management decisions.
The fundamental approach to building the CMAP Gulf of Mexico water quality monitoring, habitat monitoring, and mapping network was to: 1. Adopt, or construct as needed, a comprehensive inventory of existing habitat and water quality observation, monitoring, and mapping programs in the Gulf of Mexico (hereafter referred to as the “Inventory”; NOAA and USGS, 2019a); 2. Evaluate the suitability/applicability of each program and its existing and prospective data for use in restoration activities; 3. Develop a process to use the Inventory to conduct gap assessments; 4. Develop a catalog of baseline assessments conducted in the Gulf of Mexico (NOAA and USGS, 2019b); and 5. Develop a searchable monitoring information portal/database to enable access to collected information and products.
","language":"English","publisher":"National Oceanic and Atmospheric Administration (NOAA)","doi":"10.25923/mrdd-h727","usgsCitation":"Bosch, J., Burkart, H.B., Chivoiu, B., Clark, R., Clement, C., Enwright, N., Giordano, S., Jeffrey, C., Johnson, E., Hart, R., Hile, S.D., Howell, J.S., Laurenzano, C., Lee, M., McCloskey, T., McTigue, T., Meyers, M.B., Miller, K.E., Mize, S., Monaco, M.E., Owen, K., Rebich, R., Rendon, S.H., Robertson, A., Sample, T., Sanks, K.M., Steyer, G., Suir, K., Swarzenski, C.M., and Thurman, H.R., 2020, Council monitoring and assessment program (CMAP): A framework for using the monitoring program inventory to conduct gap assessments for the Gulf of Mexico Region: NOAA Technical Memorandum 284, ii, 55 p., https://doi.org/10.25923/mrdd-h727.","productDescription":"ii, 55 p.","startPage":"55 p.","ipdsId":"IP-119233","costCenters":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research 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Chris","contributorId":218621,"corporation":false,"usgs":false,"family":"Jeffrey","given":"Chris","email":"","affiliations":[],"preferred":false,"id":816215,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Johnson, Ed","contributorId":257936,"corporation":false,"usgs":false,"family":"Johnson","given":"Ed","email":"","affiliations":[],"preferred":false,"id":816216,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hart, Rheannon 0000-0003-4657-5945","orcid":"https://orcid.org/0000-0003-4657-5945","contributorId":218506,"corporation":false,"usgs":true,"family":"Hart","given":"Rheannon","affiliations":[{"id":39856,"text":"formerly USGS-now USACE","active":true,"usgs":false}],"preferred":false,"id":816217,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hile, Sarah D","contributorId":258255,"corporation":false,"usgs":false,"family":"Hile","given":"Sarah","email":"","middleInitial":"D","affiliations":[{"id":52263,"text":"CSS Inc.; NOAA NOS National 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0000-0001-6751-5568","orcid":"https://orcid.org/0000-0001-6751-5568","contributorId":218508,"corporation":false,"usgs":true,"family":"Mize","given":"Scott","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816225,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Monaco, Mark E.","contributorId":200279,"corporation":false,"usgs":false,"family":"Monaco","given":"Mark","email":"","middleInitial":"E.","affiliations":[{"id":12448,"text":"U.S. National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":816226,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Owen, Kevin","contributorId":218509,"corporation":false,"usgs":false,"family":"Owen","given":"Kevin","email":"","affiliations":[{"id":39855,"text":"NOAA 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Center","active":true,"usgs":true}],"preferred":true,"id":816228,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Robertson, Ali","contributorId":218623,"corporation":false,"usgs":false,"family":"Robertson","given":"Ali","email":"","affiliations":[],"preferred":false,"id":816229,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Sample, Thomas 0000-0002-3960-8334","orcid":"https://orcid.org/0000-0002-3960-8334","contributorId":218510,"corporation":false,"usgs":true,"family":"Sample","given":"Thomas","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816230,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Sanks, Kelly Marie 0000-0002-5966-2370","orcid":"https://orcid.org/0000-0002-5966-2370","contributorId":228881,"corporation":false,"usgs":true,"family":"Sanks","given":"Kelly","email":"","middleInitial":"Marie","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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cswarzen@usgs.gov","orcid":"https://orcid.org/0000-0001-9843-1471","contributorId":656,"corporation":false,"usgs":true,"family":"Swarzenski","given":"Christopher","email":"cswarzen@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":369,"text":"Louisiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816233,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Thurman, Hana Rose 0000-0001-7097-5362","orcid":"https://orcid.org/0000-0001-7097-5362","contributorId":258258,"corporation":false,"usgs":true,"family":"Thurman","given":"Hana","email":"","middleInitial":"Rose","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":816234,"contributorType":{"id":1,"text":"Authors"},"rank":30}]}} ,{"id":70216170,"text":"70216170 - 2020 - Wetlands in agricultural landscapes—Significant findings and recent advances from CEAP-Wetlands","interactions":[],"lastModifiedDate":"2020-11-07T15:59:50.088468","indexId":"70216170","displayToPublicDate":"2020-10-31T09:53:53","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Wetlands in agricultural landscapes—Significant findings and recent advances from CEAP-Wetlands","docAbstract":"The Wetlands Component of the USDA's Conservation Effects Assessment Project (CEAP-Wetlands) is a multi-agency effort advancing science related to quantifying and interpreting effects and effectiveness of conservation practices and programs on ecosystem services provided by wetlands in agricultural landscapes. This special section originated from a symposium held at the 73rd Soil and Water Conservation Society's International Annual Conference in Albuquerque New Mexico, July 29 to August 1, 2018. The symposium was jointly organized by the USDA Natural Resources Conservation Service and the US Geological Survey. To facilitate CEAP-Wetlands efforts, several regional assessments were conducted across the United States. These regional assessments were designed to address science gaps hindering wetland conservation and to develop tools facilitating conservation assessments. Conservation decisions affect not just agricultural wetlands, but also the services that these complex ecosystems provide to society. Papers in this special section of the Journal of Soil and Water Conservation present key findings and recent advances from several CEAP-Wetlands regional assessments and discuss the significant contributions of each assessment to an ever-increasing understanding of wetland ecosystems and their provisioning of ecosystem services. Modeling efforts using the Agricultural Policy and Environmental eXtender (APEX) and other process-based models are an integral component of CEAP-Wetlands. Results of these modeling efforts are also presented, and conservation implications are discussed.
Wind fetch is defined as the unobstructed distance that wind can travel over water in a constant direction. Fetches are limited by landforms surrounding the body of water. Fetch is an important characteristic of open water because longer fetches can result in larger wind-generated waves. The larger waves, in turn, can increase shoreline erosion and sediment resuspension (Rohweder and others 2012). Increases in sediment resuspension lead to increases in water turbidity, which in turn decreases light penetration and, therefore, create conditions less conducive to aquatic plant growth (Giblin and others 2010).
A wind fetch model was developed by David Finlayson, U. S. Geological Survey, Pacific Science Center, while he was a Ph.D. student at the University of Washington (Finlayson 2005). This method calculates effective fetch using the recommended procedure of the Shore Protection Manual (USACE 1984). Scientists at the United States Geological Survey, Upper Midwest Environmental Sciences Center (UMESC) and the United States Army Corps of Engineers (USACE) further refined this model (Rohweder and others 2012) and structured it to operate using the most recent version of the ArcMap Geographic Information System platform (Esri, 2019). At the time the analysis was performed, the version of ArcMap used was 10.7.1. The model refined in 2012 was used for the analyses described in this report.
Using this model, UMESC performed an analysis to model weighted wind fetch for the Upper Mississippi River System (UMRS) corresponding to three separate time periods of land cover spatial data acquisition (1989, 2000, and 2010/2011). The purpose of the analysis was to examine how fetch varies over time and space within the UMRS for potential management applications. For more detailed information on the wind fetch model, examine the USGS Open-File Report by Rohweder and others (2012).
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To understand this relationship, we assayed HCC from polar bears captured in the Alaska Beaufort, Bering and Chukchi seas during 1983–1989 and 2004–2016. Cortisol accumulated in hair through summer and autumn and into the subsequent winter. HCC was similar between adult males and adult females. No difference in HCC across regions suggested all bears responded similarly to the environment. HCC in spring was elevated following years with a high winter Arctic Oscillation index and highly variable wind speed. HCC increased non-linearly with increasing duration of the continental shelf summer open water period up to 50 days and then decreased. HCC of spring samples declined with increasing body size, indicating that the stress response was more active in smaller bears or those in poor body condition. HCC of spring samples was greater and more variable in 2004–2006 than during either 1983–1989 or 2008–2016, and significantly so for females with 1st year cubs and subadult females. Elevated HCC in 2004–2006 coincided with years of reduced survival of southern Beaufort Sea polar bears and suggests that unidentified environmental perturbations impacted Alaska polar bears. Because HCC may be obtained by relatively non-invasive means, it has potential use for assessing polar bear populations that are difficult to study by capturing. Hence, information gained from HCC can inform polar bear conservation, especially on the vulnerability of subadult females and adult females with new cubs, and possible future environmental perturbations impacts on bear physiology.","language":"English","publisher":"Northern Pacific Research Board","usgsCitation":"Durner, G.M., 2020, Using hair cortisol to assess physiological stress in Alaska polar bears: Final Report, 79 p.","productDescription":"79 p.","ipdsId":"IP-123453","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":381912,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://nprb.org/project-search/#metadata/9be4eee1-a9a4-4026-a477-02da9460d0d3/project/files"},{"id":381946,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Beaufort Sea, Bering Sea, Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.1083984375,\n 69.61120561869633\n ],\n [\n -135.17578125,\n 70.40734767606811\n ],\n [\n -140.18554687499997,\n 72.3157853052617\n ],\n [\n -154.3798828125,\n 72.93253803407026\n ],\n [\n -160.576171875,\n 73.18861179538935\n ],\n [\n -162.509765625,\n 73.20131708476698\n ],\n [\n -164.2236328125,\n 72.79008827319015\n ],\n [\n -160.7080078125,\n 71.00265967789278\n ],\n [\n -159.2578125,\n 70.8446726342528\n ],\n [\n -156.8408203125,\n 70.91664110709776\n ],\n [\n -152.0068359375,\n 70.34831755984779\n ],\n [\n -141.1083984375,\n 69.54987728327795\n ],\n [\n -141.1083984375,\n 69.61120561869633\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -168.1787109375,\n 65.6582745198266\n ],\n [\n -164.00390625,\n 66.60067571342496\n ],\n [\n -163.6083984375,\n 67.13582938531948\n ],\n [\n -166.6845703125,\n 68.39918004344189\n ],\n [\n -166.201171875,\n 69.06856318696033\n ],\n [\n -165.05859375,\n 68.95839084822076\n ],\n [\n -163.4765625,\n 69.4421276134176\n ],\n [\n -162.9052734375,\n 70.24460360904779\n ],\n [\n -167.5634765625,\n 70.88788500718185\n ],\n [\n -170.244140625,\n 70.17020068549206\n ],\n [\n -168.1787109375,\n 65.6582745198266\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -171.650390625,\n 63.68524808030715\n ],\n [\n -168.046875,\n 63.11463763252091\n ],\n [\n -167.51953124999997,\n 63.87939001720202\n ],\n [\n -169.541015625,\n 64.4348920430406\n ],\n [\n -171.38671874999997,\n 64.41592147626879\n ],\n [\n -172.3095703125,\n 64.03374392176401\n ],\n [\n -171.650390625,\n 63.68524808030715\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":807603,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70268708,"text":"70268708 - 2020 - On the robustness of annual daily precipitation maxima estimates over Monsoon Asia","interactions":[],"lastModifiedDate":"2025-07-07T16:11:01.608215","indexId":"70268708","displayToPublicDate":"2020-10-30T11:09:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21978,"text":"Frontiers in Climate Services","active":true,"publicationSubtype":{"id":10}},"title":"On the robustness of annual daily precipitation maxima estimates over Monsoon Asia","docAbstract":"Understanding precipitation extremes over Monsoon Asia is vital for water resource management and hazard mitigation, but there are many gaps and uncertainties in observations in this region. To better understand observational uncertainties, this study uses a high-resolution validation dataset to assess the consistency of the representation of annual daily precipitation maxima (Rx1day) over land in 13 observational datasets from the Frequent Rainfall Observations on Grids (FROGS) database. The FROGS datasets are grouped into three categories: in situ-based and satellite-based with and without corrections to rain gauges. We also look at three sub-regions: Japan, India, and the Maritime Continent based on their different station density, orography, and coastal complexity. We find broad similarities in spatial and temporal distributions among in situ-based products over Monsoon Asia. Satellite products with correction to rain gauges show better general agreement and less inter-product spread than their uncorrected counterparts. However, this comparison also reveals strong sub-regional differences that can be explained by the quantity and quality of rain gauges. High consistency in spatial and temporal patterns are observed over Japan, which has a dense station network, while large inter-product spread is found over the Maritime Continent and India, which have sparser station density. We also highlight that while corrected satellite products show improvement compared to uncorrected products in regions of high station density (e.g., Japan) they have mixed success over other regions (e.g., India and the Maritime Continent). In addition, the length of record available at each station can also affect the satellite correction over these poorly sampled regions. Results of the additional comparison between all considered datasets and the sub-regional high resolution dataset remain the same, indicating that the overall quality of the station network has implications for the reliability of the in situ-based products derived and also the satellite products that use a correction to in situ data. Given these uncertainties in observations, there is no single best dataset for assessment of Rx1day in Monsoon Asia. In all cases we recommend users understand how each dataset is produced in order to select the most appropriate product to estimate precipitation extremes to fit their purpose.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fclim.2020.578785","usgsCitation":"Nguyen, P., Bador, M., Alexander, L., Lane, T., and Funk, C., 2020, On the robustness of annual daily precipitation maxima estimates over Monsoon Asia: Frontiers in Climate Services, v. 2, 578785, 19 p., https://doi.org/10.3389/fclim.2020.578785.","productDescription":"578785, 19 p.","ipdsId":"IP-121958","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":492046,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fclim.2020.578785","text":"Publisher Index Page"},{"id":491743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Monsoon Asia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 67.82681208821384,\n 28.08149631086141\n ],\n [\n 67.82681208821384,\n 4.541379126404635\n ],\n [\n 88.69639230102973,\n 4.541379126404635\n ],\n [\n 88.69639230102973,\n 28.08149631086141\n ],\n [\n 67.82681208821384,\n 28.08149631086141\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 148.23729356007158,\n 45.2939171288528\n ],\n [\n 129.57288839651233,\n 45.2939171288528\n ],\n [\n 129.57288839651233,\n 29.638462684082825\n ],\n [\n 148.23729356007158,\n 29.638462684082825\n ],\n [\n 148.23729356007158,\n 45.2939171288528\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 90.4186427916805,\n 10.095537024904786\n ],\n [\n 90.4186427916805,\n -11.16935497577198\n ],\n [\n 155.06835956423896,\n -11.16935497577198\n ],\n [\n 155.06835956423896,\n 10.095537024904786\n ],\n [\n 90.4186427916805,\n 10.095537024904786\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","noUsgsAuthors":false,"publicationDate":"2020-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Nguyen, Phuong-Loan","contributorId":357544,"corporation":false,"usgs":false,"family":"Nguyen","given":"Phuong-Loan","affiliations":[{"id":85452,"text":"ARC Centre of Excellence for Climate Extremes, UNSW Sydney, Sydney, New South Wales, Australia","active":true,"usgs":false}],"preferred":false,"id":941695,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bador, Margot","contributorId":223056,"corporation":false,"usgs":false,"family":"Bador","given":"Margot","email":"","affiliations":[{"id":40656,"text":"Climate Change Research Centre, UNSW Sydney","active":true,"usgs":false}],"preferred":false,"id":941696,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Lisa","contributorId":223054,"corporation":false,"usgs":false,"family":"Alexander","given":"Lisa","email":"","affiliations":[{"id":40656,"text":"Climate Change Research Centre, UNSW Sydney","active":true,"usgs":false}],"preferred":false,"id":941697,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lane, Todd P.","contributorId":357545,"corporation":false,"usgs":false,"family":"Lane","given":"Todd P.","affiliations":[{"id":85454,"text":"2School of Earth Science and ARC Centre of Excellence for Climate Extremes, The University of Melbourne, Melbourne, Victoria, Australia","active":true,"usgs":false}],"preferred":false,"id":941698,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Funk, Chris 0000-0002-9254-6718 cfunk@usgs.gov","orcid":"https://orcid.org/0000-0002-9254-6718","contributorId":167070,"corporation":false,"usgs":true,"family":"Funk","given":"Chris","email":"cfunk@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":941699,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215989,"text":"70215989 - 2020 - Farmer behavior under groundwater management scenarios: Implications for groundwater conservation in the Mississippi Alluvial Plain","interactions":[],"lastModifiedDate":"2020-11-04T12:41:41.849604","indexId":"70215989","displayToPublicDate":"2020-10-30T08:01:49","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7347,"text":"Water Economics and Policy","active":true,"publicationSubtype":{"id":10}},"title":"Farmer behavior under groundwater management scenarios: Implications for groundwater conservation in the Mississippi Alluvial Plain","docAbstract":"Concern about sustained availability of fresh groundwater for agricultural use in the Mississippi Alluvial Plain (MAP) mounts as groundwater levels decline. We evaluate elasticities of demand for groundwater and other agricultural inputs, as well as overall and output specific economies of scale for four major irrigated commodities (rice, corn, soybeans, and cotton) in the MAP region. Additionally, we investigate impacts of two groundwater management policy scenarios, including increasing pumping cost and groundwater use restrictions, on irrigation behavior. The results show price elasticity of demand for groundwater to be -0.13, indicating that it is inelastic, and an increasing cost of pumping will not significantly decrease relative demand for groundwater in the region. Even with policy scenarios that either increase the costs of pumping significantly or restrict groundwater use in the region, groundwater demand still appears to be inelastic. We also document significant overall economies of scale in the region. Our findings have implications for potential policy options aimed at reducing groundwater use. Efficient management practices are important to increase aquifer recharge, and at the same time, incorporation of human behavior via economic analysis will improve projections of groundwater availability in the MAP region.","language":"English","publisher":"World Scientific Publications","doi":"10.1142/S2382624X20500095","usgsCitation":"Alhassan, M., Pindilli, E., and Lawrence, C., 2020, Farmer behavior under groundwater management scenarios: Implications for groundwater conservation in the Mississippi Alluvial Plain: Water Economics and Policy, v. 6, no. 4, https://doi.org/10.1142/S2382624X20500095.","ipdsId":"IP-117014","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":380072,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Tennessee, Arkansas, Louisiana, Mississippi","otherGeospatial":"Mississippi Alluvial Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.6484375,\n 37.31775185163688\n ],\n [\n -91.5380859375,\n 34.17999758688084\n ],\n [\n -92.4169921875,\n 31.970803930433096\n ],\n [\n -92.04345703125,\n 30.240086360983426\n ],\n [\n -90.76904296874999,\n 30.56226095049944\n ],\n [\n -89.47265625,\n 34.88593094075317\n ],\n [\n -89.033203125,\n 36.50963615733049\n ],\n [\n -89.6484375,\n 37.31775185163688\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"6","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-10-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Alhassan, Mustapha 0000-0001-6201-0077","orcid":"https://orcid.org/0000-0001-6201-0077","contributorId":244289,"corporation":false,"usgs":false,"family":"Alhassan","given":"Mustapha","affiliations":[{"id":6736,"text":"Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":803693,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pindilli, Emily 0000-0002-5101-1266 epindilli@usgs.gov","orcid":"https://orcid.org/0000-0002-5101-1266","contributorId":140262,"corporation":false,"usgs":true,"family":"Pindilli","given":"Emily","email":"epindilli@usgs.gov","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":803694,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lawrence, Collin B 0000-0001-9224-5774","orcid":"https://orcid.org/0000-0001-9224-5774","contributorId":244290,"corporation":false,"usgs":false,"family":"Lawrence","given":"Collin B","affiliations":[{"id":48882,"text":"Department of the Navy","active":true,"usgs":false}],"preferred":false,"id":803695,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215785,"text":"sir20205094 - 2020 - Geochemical assessment of groundwater in the Big Chino subbasin, Arizona, 2011–18","interactions":[],"lastModifiedDate":"2020-10-30T15:26:07.378654","indexId":"sir20205094","displayToPublicDate":"2020-10-29T20:57:35","publicationYear":"2020","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":"2020-5094","displayTitle":"Geochemical Assessment of Groundwater in the Big Chino Subbasin, Arizona, 2011–18","title":"Geochemical assessment of groundwater in the Big Chino subbasin, Arizona, 2011–18","docAbstract":"A geochemical characterization of groundwater in the Big Chino subbasin of Arizona was conducted by the U.S. Geological Survey, in cooperation with the City of Prescott, the Town of Prescott Valley, and the Salt River Project, to understand groundwater evolution through the study area and the source of water to springs along the gaining reach of the Verde River just downstream from its confluence with Granite Creek. Samples were collected between 2011 and 2018 in groundwater wells completed in basin-fill and carbonate aquifers and at selected springs, including two discrete springs discharging along the aforementioned stretch of the Verde River. Five newly installed monitoring wells completed in the carbonate aquifer were sampled in 2018. Water-quality results obtained from these samples include the first known geochemical data for carbonate groundwater beneath the basin-fill in the Big Chino subbasin downgradient from Walnut Creek near Paulden, Arizona, as well as other parts of the study area without previous data. Groundwater samples were collected and analyzed for major ions, arsenic, nutrients, stable isotopes of oxygen and hydrogen (δ18O and δ2H), strontium isotopes (87Sr/86Sr), carbon-14, isotopes of carbon (δ13C), and noble gases.
Significant differences in groundwater geochemistry between the basin-fill and carbonate aquifers were driven primarily by higher pH, tritium, and δ18O and δ2H in the basin-fill aquifer samples and higher specific conductance and higher concentrations of calcium, sodium, bicarbonate, fluoride, and arsenic in the carbonate aquifer samples. All but one sample from the carbonate aquifer and two samples from the basin-fill aquifer exceeded the U.S. Environmental Protection Agency (EPA) drinking water standard for arsenic of 10 micrograms per liter. One basin-fill aquifer sample exceeded the EPA drinking water standard for fluoride of 4 milligrams per liter, and one carbonate aquifer sample exceeded the EPA secondary drinking water standard for fluoride of 2 milligrams per liter. A component of modern groundwater recharged following aboveground nuclear testing beginning in the mid-1950s is present in some basin-fill and spring groundwater from this study. Groundwater that can be dated using radiocarbon decay is also present in the study area, with four groundwater samples indicating possible recharge during the Pleistocene with groundwater ages ranging from approximately 34,600 to 13,300 years before present. Other groundwater sampled during this study that can dated using radiocarbon decay ranged in age from about 7,500 to 1,100 years before present, indicating possible recharge during the Holocene.
The gaining reach of the Verde River downstream from the confluence with Granite Creek shows areal changes in temperature, pH, and specific conductance, indicating multiple zones of groundwater input. Surface-water samples for analyses of δ18O and δ2H have been collected at the Verde River near Paulden, Ariz. streamgage (09503700) during discharge measurements since 2009, and a trend analysis of the δ18O and δ2H data indicated no significant trend exists for the 10-year period of record. Additional groundwater samples from the carbonate aquifer beneath the basin-fill upgradient and downgradient from Walnut Creek would provide valuable information to understand groundwater evolution along the Big Chino subbasin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205094","collaboration":"Prepared in cooperation with the City of Prescott, the Town of Prescott Valley, and the Salt River Project","usgsCitation":"Beisner, K.R., and Jones, C.J.R., 2020, Geochemical assessment of groundwater in the Big Chino subbasin, Arizona, 2011–18: U.S. Geological Survey Scientific Investigations Report 2020–5094, 49 p., https://doi.org/10.3133/sir20205094.","productDescription":"Report: viii, 49 p.; 2 Appendixes; 2 Data Releases","numberOfPages":"61","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113409","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":379927,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HMZNIK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Carbon and strontium isotopic data for rock, soil, and soil gas from the Big Chino Sub-Basin, Arizona, 2017 and 2018"},{"id":379924,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094_appendix_1.csv","text":"Appendix 1","size":"14.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2020–5094 Appendix 1","linkHelpText":"— Groundwater Geochemistry Data for Samples Collected by the U.S. Geological Survey from the Big Chino Subbasin Between 2011 and 2018"},{"id":379923,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094.pdf","text":"Report","size":"37.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5094"},{"id":379926,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P909LD47","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water quality parameters in the Verde River below Granite Creek, Arizona, June 2018"},{"id":379922,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5094/coverthb.jpg"},{"id":379925,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5094/sir20205094_appendix_1.xlsx","text":"Appendix 1","size":"33.8 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2020–5094 Appendix 1","linkHelpText":"— Groundwater Geochemistry Data for Samples Collected by the U.S. Geological Survey from the Big Chino Subbasin Between 2011 and 2018"}],"country":"United States","state":"Arizona","otherGeospatial":"Big Chino subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.18389892578125,\n 34.3366324743773\n ],\n [\n -111.8463134765625,\n 34.3366324743773\n ],\n [\n -111.8463134765625,\n 35.1154153142536\n ],\n [\n -113.18389892578125,\n 35.1154153142536\n ],\n [\n -113.18389892578125,\n 34.3366324743773\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
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6700 Edith Blvd NE
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We present measurements of the density, hydraulic conductivity, and specific discharge of a widespread firn aquifer in Antarctica, within the Wilkins Ice Shelf. At the field site, the aquifer is 16.2 m thick, starting at 13.4 m from the snow surface and transitioning from water‐saturated firn to ice at 29.6 m. Hydraulic conductivity derived from slug tests show a geometric mean value of 1.4 ± 1.2 × 10−4 m s−1, equivalent to permeability of 2.6 ± 2.2 × 10−11 m2. A borehole dilution test indicates an average specific discharge value of 1.9 ± 2.8 × 10−6 m s−1. Ground‐penetrating radar profiles and a groundwater flow model show the aquifer is draining laterally into a large nearby rift. Our findings indicate that the firn aquifer in the vicinity of the field site is likely not in a steady state and its presence likely contributed to past ice shelf instability.
","language":"English","publisher":"Wiley","doi":"10.1029/2020GL089552","usgsCitation":"Montgomery, L., Miege, C., MIller, J., Wallin, B., Miller, O.L., Scambos, T.A., Solomon, D., Forster, R., and Koenig, L., 2020, Hydrologic properties of a highly permeable firn aquifer in the Wilkins Ice Shelf, Antarctica: Geophysical Research Letters, v. 47, e2020GL089552, 10 p., https://doi.org/10.1029/2020GL089552.","productDescription":"e2020GL089552, 10 p.","ipdsId":"IP-119455","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":454923,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2020gl089552","text":"External Repository"},{"id":382525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Wilkins Ice Sheet","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.54022216796875,\n -71.79883675782347\n ],\n [\n -70.400390625,\n -71.79883675782347\n ],\n [\n -70.400390625,\n -71.54143894204527\n ],\n [\n -71.54022216796875,\n -71.54143894204527\n ],\n [\n -71.54022216796875,\n -71.79883675782347\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationDate":"2020-11-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Montgomery, Lynn","contributorId":244036,"corporation":false,"usgs":false,"family":"Montgomery","given":"Lynn","email":"","affiliations":[{"id":36627,"text":"University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":803105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miege, C.","contributorId":248303,"corporation":false,"usgs":false,"family":"Miege","given":"C.","email":"","affiliations":[],"preferred":false,"id":808855,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"MIller, Julie","contributorId":248311,"corporation":false,"usgs":false,"family":"MIller","given":"Julie","email":"","affiliations":[],"preferred":false,"id":808856,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wallin, Bruce","contributorId":248312,"corporation":false,"usgs":false,"family":"Wallin","given":"Bruce","email":"","affiliations":[],"preferred":false,"id":808857,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Scambos, Ted A.","contributorId":57367,"corporation":false,"usgs":true,"family":"Scambos","given":"Ted","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":808858,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miller, Olivia L. 0000-0002-8846-7048","orcid":"https://orcid.org/0000-0002-8846-7048","contributorId":216556,"corporation":false,"usgs":true,"family":"Miller","given":"Olivia","email":"","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803106,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Solomon, D Kip","contributorId":146290,"corporation":false,"usgs":false,"family":"Solomon","given":"D Kip","affiliations":[{"id":7215,"text":"University of Utah Dept. of Geography","active":true,"usgs":false}],"preferred":false,"id":808859,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Forster, Richard","contributorId":172149,"corporation":false,"usgs":false,"family":"Forster","given":"Richard","affiliations":[{"id":26993,"text":"University of Utah, Department of Geography","active":true,"usgs":false}],"preferred":false,"id":808860,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Koenig, Lora","contributorId":248313,"corporation":false,"usgs":false,"family":"Koenig","given":"Lora","affiliations":[],"preferred":false,"id":808861,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215809,"text":"70215809 - 2020 - Differences in neonicotinoid and metabolite sorption to activated carbon are driven by alterations to the insecticidal pharmacophore","interactions":[],"lastModifiedDate":"2020-11-30T16:19:54.876291","indexId":"70215809","displayToPublicDate":"2020-10-29T09:16:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Differences in neonicotinoid and metabolite sorption to activated carbon are driven by alterations to the insecticidal pharmacophore","docAbstract":"Widespread application of neonicotinoids has led to their proliferation in waters. Despite low neonicotinoid hydrophobicity, our prior studies implicated granular activated carbon (GAC) in neonicotinoid removal. Based on known receptor binding characteristics, we hypothesized that the insecticidal pharmacophore influences neonicotinoid sorption. Our objectives were to illuminate drivers of neonicotinoid sorption for parent neonicotinoids (imidacloprid, clothianidin, thiamethoxam, and thiacloprid) and pharmacophore-altered metabolites (desnitro-imidacloprid and imidacloprid urea) to GAC, powdered activated carbon, and carbon nanotubes (CNTs). Neonicotinoid sorption to GAC was extensive and largely irreversible, with significantly greater sorption of imidacloprid than desnitro-imidacloprid. Imidacloprid and imidacloprid urea (electronegative pharmacophores) sorbed most extensively to nonfunctionalized CNTs, whereas desnitro-imidacloprid (positive pharmacophore) sorbed most to COOH-CNTs, indicating the importance of charge interactions and/or hydrogen bonding between the pharmacophore and carbon surface. Water chemistry parameters (temperature, alkalinity, ionic strength, and humic acid) inhibited overall neonicotinoid sorption, suggesting that pharmacophore-driven sorption in real waters may be diminished. Analysis of a full-scale drinking water treatment plant GAC filter influent, effluent, and spent GAC attributes neonicotinoid/metabolite removal to GAC under real-world conditions for the first time. Our results demonstrate that the neonicotinoid pharmacophore not only confers insecticide selectivity but also impacts sorption behavior, leading to less effective removal of metabolites by GAC filters in water treatment.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.0c04187","usgsCitation":"Webb, D.T., Nagorzanski, M.R., Powers, M.M., Cwiertny, D.M., Hladik, M.L., and LeFevre, G.H., 2020, Differences in neonicotinoid and metabolite sorption to activated carbon are driven by alterations to the insecticidal pharmacophore: Environmental Science and Technology, v. 54, no. 22, p. 14694-14705, https://doi.org/10.1021/acs.est.0c04187.","productDescription":"10 p.","startPage":"14694","endPage":"14705","onlineOnly":"N","ipdsId":"IP-119721","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":379964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"22","noUsgsAuthors":false,"publicationDate":"2020-10-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Webb, Danielle T.","contributorId":211879,"corporation":false,"usgs":false,"family":"Webb","given":"Danielle","email":"","middleInitial":"T.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":803520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nagorzanski, Matthew R.","contributorId":211881,"corporation":false,"usgs":false,"family":"Nagorzanski","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":803521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Powers, Megan M","contributorId":244212,"corporation":false,"usgs":false,"family":"Powers","given":"Megan","email":"","middleInitial":"M","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":false,"id":803522,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cwiertny, David M.","contributorId":190557,"corporation":false,"usgs":false,"family":"Cwiertny","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":803523,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":205314,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803524,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"LeFevre, Gregory H.","contributorId":211880,"corporation":false,"usgs":false,"family":"LeFevre","given":"Gregory","email":"","middleInitial":"H.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":true,"id":803525,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220554,"text":"70220554 - 2020 - Salinity and inundation effects on productivity of brackish tidal marsh plants in the San Francisco Bay-Delta Estuary","interactions":[],"lastModifiedDate":"2021-05-20T12:10:27.179639","indexId":"70220554","displayToPublicDate":"2020-10-29T07:57:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Salinity and inundation effects on productivity of brackish tidal marsh plants in the San Francisco Bay-Delta Estuary","docAbstract":"Plant productivity is central to numerous ecosystem functions in tidal wetlands. We examined how productivity of brackish marsh plants in northern California responded to abiotic stress gradients of inundation and salinity using two experimental approaches. In a greenhouse study with varying salinity, shoot production and biomass of Juncus balticus, Schoenoplectus acutus and S. americanus all declined monotonically with higher salinity, with evidence of differences in sensitivity among species by their varied functional responses. Salinity also negatively affected fecundity for the one species (S. americanus) that produced enough inflorescences during the experiment for analysis. In a field manipulation of inundation and initial pore water salinity, total end-of-season biomass and other metrics of growth in the high marsh species, J. balticus, had unimodal relationships with inundation. Root production tended to be greater strongly impacted by greater inundation than shoot production. The salinity treatment quickly dissipated for treatments that were flooded more frequently but persisted at a higher marsh elevation where it suppressed plant growth. These results suggest that both increased flooding and salinity associated with climate change and sea-level rise may negatively impact productivity of brackish marsh species, but with variable effects by species and stressor.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-020-04419-3","usgsCitation":"Janousek, C.N., Dugger, B.D., Drucker, B.M., and Thorne, K., 2020, Salinity and inundation effects on productivity of brackish tidal marsh plants in the San Francisco Bay-Delta Estuary: Hydrobiologia, v. 847, p. 4311-4323, https://doi.org/10.1007/s10750-020-04419-3.","productDescription":"13 p.","startPage":"4311","endPage":"4323","ipdsId":"IP-122239","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":385759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"San Francisco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.134765625,\n 36.84446074079564\n ],\n [\n -120.9814453125,\n 36.84446074079564\n ],\n [\n -120.9814453125,\n 39.232253141714885\n ],\n [\n -123.134765625,\n 39.232253141714885\n ],\n [\n -123.134765625,\n 36.84446074079564\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"847","noUsgsAuthors":false,"publicationDate":"2020-10-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Janousek, Christopher N. 0000-0003-2124-6715","orcid":"https://orcid.org/0000-0003-2124-6715","contributorId":103951,"corporation":false,"usgs":false,"family":"Janousek","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":815986,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dugger, Bruce D.","contributorId":176167,"corporation":false,"usgs":false,"family":"Dugger","given":"Bruce","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":815987,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drucker, Brandon M","contributorId":258214,"corporation":false,"usgs":false,"family":"Drucker","given":"Brandon","email":"","middleInitial":"M","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":815988,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thorne, Karen M. 0000-0002-1381-0657","orcid":"https://orcid.org/0000-0002-1381-0657","contributorId":204579,"corporation":false,"usgs":true,"family":"Thorne","given":"Karen M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815989,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215985,"text":"70215985 - 2020 - Nitrate in streams during winter low‐flow conditions as an indicator of legacy nitrate","interactions":[],"lastModifiedDate":"2020-11-30T16:30:57.387972","indexId":"70215985","displayToPublicDate":"2020-10-29T07:48:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Nitrate in streams during winter low‐flow conditions as an indicator of legacy nitrate","docAbstract":"Winter low‐flow (LF) conditions in streams provide a potential opportunity to evaluate the importance of legacy nitrate in catchments due to the dominance of slow‐flow transport pathways and lowered biotic activity. In this study, the concentration, flux, and trend of nitrate in streams during winter low‐flow conditions were analyzed at 320 sites in the conterminous United States. LF flow‐normalized nitrate concentrations varied from <0.1 to >20 mg‐N L‐1 and LF conditions contributed between 2% and 98% of the winter nitrate flux. LF nitrate concentrations generally exceeded 2.5 mg‐N L‐1 in the upper Midwest, with smaller regions of high LF nitrate concentrations in eastern Texas and along the northern mid‐Atlantic coast. Groundwater was inferred to be the primary or sole contributor of nitrate to streams during winter LF conditions at 140 of our 320 sites. Among these 140 sites, nitrate from groundwater comprised 45% or more of the winter nitrate flux at a quarter of the sites. Among the same 140 sites, concentrations of nitrate in streams during winter LF conditions generally increased between 2002 and 2012 at sites where 40% or more of the winter flux was from groundwater, suggesting that concentrations of nitrate in the contributing groundwater system were increasing. Using metrics developed herein, we characterize the potential importance of legacy nitrate at sites in this study and discuss methods to characterize sites with fewer samples than required by our models or at sites without continuous stream discharge measurements.
Modeling forest change effects on snow is critical to resource management. However, many models either do not appropriately model canopy structure or cannot represent fine‐scale changes in structure following a disturbance. We applied a 1 m2 resolution energy budget snowpack model at a forested site in New Mexico, USA, affected by a wildfire, using input data from lidar to represent prefire and postfire canopy conditions. Both scenarios were forced with 37 years of equivalent meteorology to simulate the effect of fire‐mediated canopy change on snowpack under varying meteorology. Postfire, the simulated snow distribution was substantially altered, and despite an overall increase in snow, 32% of the field area displayed significant decreases, resulting in higher snowpack variability. The spatial differences in snow were correlated with the change in several direction‐based forest structure metrics (aspect‐based canopy edginess and gap area). Locations with decreases in snow following the fire were on southern aspects that transitioned to south facing canopy edges, canopy gaps that increased in size to the south, or where large trees were removed. Locations with largest increases in snow occurred where all canopy was removed. Changes in canopy density metrics, typically used in snow models to represent the forest, did not fully explain the effects of fire on snow distribution. This explains why many models are not able to represent greater postfire variability in snow distribution and tend to predict only increases in snowpack following a canopy disturbance event despite observational studies showing both increases and decreases.
In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in‐stream water quality and process interactions, soil health and land management, and (peri‐)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches, and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure, and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020WR027721","usgsCitation":"Fu, B., Horsburgh, J., Jakeman, A.J., Gaultieri, C., Arnold, T.W., Marshall, L.A., Green, T.R., Quinn, N.W., Volk, M., Hunt, R., Vezzaro, L., Croke, B., Jakeman, J., Snow, V.O., and Rashleigh, B., 2020, Modeling water quality in watersheds: From here to the next generation: Water Resources Research, v. 56, no. 11, e2020WR027721, 28 p., https://doi.org/10.1029/2020WR027721.","productDescription":"e2020WR027721, 28 p.","ipdsId":"IP-123332","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":454942,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020wr027721","text":"Publisher Index Page"},{"id":383140,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Fu, Baihua 0000-0003-2494-0518","orcid":"https://orcid.org/0000-0003-2494-0518","contributorId":174165,"corporation":false,"usgs":false,"family":"Fu","given":"Baihua","email":"","affiliations":[],"preferred":false,"id":810074,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Horsburgh, J. S. 0000-0002-0768-3196","orcid":"https://orcid.org/0000-0002-0768-3196","contributorId":248851,"corporation":false,"usgs":false,"family":"Horsburgh","given":"J. S.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":810075,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jakeman, Anthony J. 0000-0001-5282-2215","orcid":"https://orcid.org/0000-0001-5282-2215","contributorId":173848,"corporation":false,"usgs":false,"family":"Jakeman","given":"Anthony","email":"","middleInitial":"J.","affiliations":[{"id":17939,"text":"The Australian National University","active":true,"usgs":false}],"preferred":false,"id":810076,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gaultieri, C 0000-0002-3717-1618","orcid":"https://orcid.org/0000-0002-3717-1618","contributorId":248852,"corporation":false,"usgs":false,"family":"Gaultieri","given":"C","email":"","affiliations":[{"id":50045,"text":"University of Napoli","active":true,"usgs":false}],"preferred":false,"id":810077,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arnold, Todd W.","contributorId":36058,"corporation":false,"usgs":false,"family":"Arnold","given":"Todd","email":"","middleInitial":"W.","affiliations":[{"id":12644,"text":"University of Minnesota, St. Paul","active":true,"usgs":false}],"preferred":false,"id":810078,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marshall, Lucy A. 0000-0003-0450-4292","orcid":"https://orcid.org/0000-0003-0450-4292","contributorId":198080,"corporation":false,"usgs":false,"family":"Marshall","given":"Lucy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":810079,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Green, Tim R 0000-0002-1441-8008","orcid":"https://orcid.org/0000-0002-1441-8008","contributorId":248853,"corporation":false,"usgs":false,"family":"Green","given":"Tim","email":"","middleInitial":"R","affiliations":[{"id":39550,"text":"U.S. Department of Agriculture, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":810080,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Quinn, Nigel W. T. 0000-0003-3333-4763","orcid":"https://orcid.org/0000-0003-3333-4763","contributorId":248854,"corporation":false,"usgs":false,"family":"Quinn","given":"Nigel","email":"","middleInitial":"W. T.","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":810081,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Volk, Martin 0000-0003-0064-8133","orcid":"https://orcid.org/0000-0003-0064-8133","contributorId":247479,"corporation":false,"usgs":false,"family":"Volk","given":"Martin","email":"","affiliations":[{"id":13477,"text":"Washington Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":810082,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":16118,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810083,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Vezzaro, L. 0000-0001-6344-7131","orcid":"https://orcid.org/0000-0001-6344-7131","contributorId":248855,"corporation":false,"usgs":false,"family":"Vezzaro","given":"L.","affiliations":[{"id":50046,"text":"Technical University of Denmark","active":true,"usgs":false}],"preferred":false,"id":810084,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Croke, Barry 0000-0001-9216-1554","orcid":"https://orcid.org/0000-0001-9216-1554","contributorId":248856,"corporation":false,"usgs":false,"family":"Croke","given":"Barry","email":"","affiliations":[{"id":27305,"text":"Australia National University","active":true,"usgs":false}],"preferred":false,"id":810085,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Jakeman, John 0000-0002-3517-337X","orcid":"https://orcid.org/0000-0002-3517-337X","contributorId":248857,"corporation":false,"usgs":false,"family":"Jakeman","given":"John","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":810086,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Snow, Valerie O 0000-0002-6911-8184","orcid":"https://orcid.org/0000-0002-6911-8184","contributorId":248846,"corporation":false,"usgs":false,"family":"Snow","given":"Valerie","email":"","middleInitial":"O","affiliations":[{"id":50044,"text":"AgResearch","active":true,"usgs":false}],"preferred":false,"id":810087,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Rashleigh, Brenda 0000-0002-0806-686X","orcid":"https://orcid.org/0000-0002-0806-686X","contributorId":242708,"corporation":false,"usgs":false,"family":"Rashleigh","given":"Brenda","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":810088,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70216104,"text":"70216104 - 2020 - Assessment of burrowing behavior of freshwater juvenile mussels in sediment","interactions":[],"lastModifiedDate":"2020-11-06T12:49:54.295425","indexId":"70216104","displayToPublicDate":"2020-10-28T08:23:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5254,"text":"Freshwater Mollusk Biology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of burrowing behavior of freshwater juvenile mussels in sediment","docAbstract":"Standard laboratory sediment toxicity methods have been adapted for conducting toxicity tests with juvenile freshwater mussels. However, studies looking at juvenile mussel burrowing behavior at the water-sediment interface are limited. Juvenile mussels burrow in sediment for the first 0 to 4 yr of life but also may inhabit the sediment-water interface. The objective of this study was to evaluate burrowing behavior of various species and ages of juvenile freshwater mussels in three control sediments: West Bearskin Lake, Spring River, and coarse commercial sand. Species tested included (1) Fatmucket (Lampsilis siliquoidea), (2) Notched Rainbow (Villosa constricta), (3) Washboard (Megalonaias nervosa), (4) Rainbow (Villosa iris), (5) Arkansas Fatmucket (Lampsilis powellii), and (6) Oregon Floater (Anodonta oregonensis). Greater than 95% of the mussels burrowed into test sediment within 15 min. Across species, life stage, and substrate type, most mussels were recovered from the upper layers of sediment (91% at a sediment depth of 3.4 mm or less), and only 2% of the mussels were recovered at a depth >5.1 mm. No mussels were recovered from a depth >6.8 mm. There was no difference in mussel burrowing depth at 4 h versus 24 h across species, age, and sediment type. Two ages of Fatmucket burrowed to a significantly greater depth in the West Bearskin Lake sediment compared to the Spring River sediment or Coarse Sand. However, there was no significant difference in mean depth across sediment type with the other five species of mussels tested. Based on species and age of mussels tested, juvenile mussels up to an age of at least 20 wk and a length of at least 5 mm readily burrow into sediment and likely would be exposed to contaminants in whole sediment and associated pore water throughout a laboratory sediment toxicity test.
","language":"English","publisher":"BioOne","doi":"10.31931/fmbc.v23i2.2020.69-81","usgsCitation":"Kemble, N.E., Besser, J.M., Steevens, J.A., and Hughes, J., 2020, Assessment of burrowing behavior of freshwater juvenile mussels in sediment: Freshwater Mollusk Biology and Conservation, v. 23, no. 2, p. 69-81, https://doi.org/10.31931/fmbc.v23i2.2020.69-81.","productDescription":"13 p.","startPage":"69","endPage":"81","ipdsId":"IP-105537","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":454951,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc.v23i2.2020.69-81","text":"Publisher Index Page"},{"id":436739,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NTLG30","text":"USGS data release","linkHelpText":"Burrowing behavior of freshwater mussels"},{"id":380186,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kemble, Nile E. 0000-0002-3608-0538 nkemble@usgs.gov","orcid":"https://orcid.org/0000-0002-3608-0538","contributorId":2626,"corporation":false,"usgs":true,"family":"Kemble","given":"Nile","email":"nkemble@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":804103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Besser, John M. 0000-0002-9464-2244 jbesser@usgs.gov","orcid":"https://orcid.org/0000-0002-9464-2244","contributorId":2073,"corporation":false,"usgs":true,"family":"Besser","given":"John","email":"jbesser@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":804104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":804105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hughes, Jamie P.","contributorId":244522,"corporation":false,"usgs":false,"family":"Hughes","given":"Jamie P.","affiliations":[{"id":48808,"text":"Veterans United, Columbia MO","active":true,"usgs":false}],"preferred":false,"id":804106,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216063,"text":"70216063 - 2020 - Topographic, soil, and climate drivers of drought sensitivity in forests and shrublands of the Pacific Northwest, USA","interactions":[],"lastModifiedDate":"2020-11-04T13:28:33.960827","indexId":"70216063","displayToPublicDate":"2020-10-28T07:24:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Topographic, soil, and climate drivers of drought sensitivity in forests and shrublands of the Pacific Northwest, USA","docAbstract":"Climate change is anticipated to increase the frequency and intensity of droughts, with major impacts to ecosystems globally. Broad-scale assessments of vegetation responses to drought are needed to anticipate, manage, and potentially mitigate climate-change effects on ecosystems. We quantified the drought sensitivity of vegetation in the Pacific Northwest, USA, as the percent reduction in vegetation greenness under droughts relative to baseline moisture conditions. At a regional scale, shrub-steppe ecosystems—with drier climates and lower biomass—showed greater drought sensitivity than conifer forests. However, variability in drought sensitivity was considerable within biomes and within ecosystems and was mediated by landscape topography, climate, and soil characteristics. Drought sensitivity was generally greater in areas with higher elevation, drier climate, and greater soil bulk density. Ecosystems with high drought sensitivity included dry forests along ecotones to shrublands, Rocky Mountain subalpine forests, and cold upland sagebrush communities. In forests, valley bottoms and areas with low soil bulk density and high soil available water capacity showed reduced drought sensitivity, suggesting their potential as drought refugia. These regional-scale drought-sensitivity patterns discerned from remote sensing can complement plot-scale studies of plant physiological responses to drought to help inform climate-adaptation planning as drought conditions intensify.
","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-020-75273-5","usgsCitation":"Cartwright, J.M., Littlefield, C.E., Michalak, J., Lawler, J.J., and Dobrowski, S., 2020, Topographic, soil, and climate drivers of drought sensitivity in forests and shrublands of the Pacific Northwest, USA: Scientific Reports, v. 10, 18486, 13 p., https://doi.org/10.1038/s41598-020-75273-5.","productDescription":"18486, 13 p.","ipdsId":"IP-105631","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":454954,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-75273-5","text":"Publisher Index Page"},{"id":436741,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UNYG2R","text":"USGS data release","linkHelpText":"Analysis of drought sensitivity in the Pacific Northwest (Washington, Oregon, and Idaho) from 2000 through 2016"},{"id":436740,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9UNYG2R","text":"USGS data release","linkHelpText":"Analysis of drought sensitivity in the Pacific Northwest (Washington, Oregon, and Idaho) from 2000 through 2016"},{"id":380119,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington, Oregon, Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.62890625,\n 48.45835188280866\n ],\n [\n -124.76074218749999,\n 41.86956082699455\n ],\n [\n -110.91796875,\n 41.83682786072714\n ],\n [\n -111.09374999999999,\n 45.089035564831036\n ],\n [\n -113.37890625,\n 44.87144275016589\n ],\n [\n -116.27929687499999,\n 49.06666839558117\n ],\n [\n -123.3984375,\n 49.009050809382046\n ],\n [\n -124.62890625,\n 48.45835188280866\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":803898,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Littlefield, Caitlin E. 0000-0003-3771-7956","orcid":"https://orcid.org/0000-0003-3771-7956","contributorId":220623,"corporation":false,"usgs":false,"family":"Littlefield","given":"Caitlin","email":"","middleInitial":"E.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":803899,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Michalak, Julia 0000-0002-2524-8390","orcid":"https://orcid.org/0000-0002-2524-8390","contributorId":210589,"corporation":false,"usgs":false,"family":"Michalak","given":"Julia","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":803900,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lawler, Joshua J.","contributorId":73327,"corporation":false,"usgs":false,"family":"Lawler","given":"Joshua","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":803901,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dobrowski, Solomon","contributorId":229621,"corporation":false,"usgs":false,"family":"Dobrowski","given":"Solomon","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":803902,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}