The Old Yuma Mine is an abandoned copper, lead, zinc, silver, and gold mine located within the boundaries of Saguaro National Park, Tucson Mountain District, Arizona. This study analyzed the geochemistry of sediments associated with the Old Yuma Mine and assessed hydrologic and geochemical conditions of groundwater to evaluate the area surrounding the Old Yuma Mine. The purpose of the study was to establish the geochemical signature of material associated with the Old Yuma Mine and to compare it with background material and groundwater in the area. Few groundwater samples exceeded the U.S. Environmental Protection Agency (EPA) drinking water standards. Concentrations of several elements were elevated in the waste rock and mine tailings compared with concentrations in sediments collected in background areas. A subset of 15 sediment samples was leached to simulate precipitation interacting with the solid material. Analysis of leachate samples compared to groundwater samples suggests that groundwater samples collected in this study are distinct from leachate samples associated with mining related material. Results suggest that at this time groundwater samples collected during this investigation are not influenced by elements leached from Old Yuma Mine materials.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185019","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Beisner, K.R., and Gray, F., 2018, Assessment of geochemical and hydrologic conditions near Old Yuma Mine in Saguaro National Park, Arizona, 2014–17: U.S. Geological Survey Scientific Investigations Report 2018–5019, 52 p., https://doi.org/10.3133/sir20185019.","productDescription":"Report: vii, 52 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-088828","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":352442,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5019/sir20185019.pdf","text":"Report","size":"5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5019"},{"id":352441,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5019/coverthb.jpg"},{"id":352443,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5019/appendixa_sir20185019.xlsx","text":"Appendix A","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2018-5019 Appendix","linkHelpText":" - Groundwater sample data from Old Yuma Mine study area"}],"country":"United States","state":"Arizona","otherGeospatial":" Old Yuma Mine, Saguaro National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111,\n 32.34\n ],\n [\n -111.17,\n 32.34\n ],\n [\n -111.17,\n 32.25\n ],\n [\n -111,\n 32.25\n ],\n [\n -111,\n 32.34\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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The water quality of the Chesapeake Bay may be adversely affected by dissolved nitrate carried in groundwater discharge to streams. To estimate the concentrations, loads, and yields of nitrate from groundwater to streams for the Chesapeake Bay watershed, a regression model was developed based on measured nitrate concentrations from 156 small streams with watersheds less than 500 square miles (mi2 ) at baseflow. The regression model has three predictive variables: geologic unit, percent developed land, and percent agricultural land. Comparisons of estimated and actual values within geologic units were closely matched. The coefficient of determination (R2 ) for the model was 0.6906. The model was used to calculate baseflow nitrate concentrations at over 83,000 National Hydrography Dataset Plus Version 2 catchments and aggregated to 1,966 total 12-digit hydrologic units in the Chesapeake Bay watershed. The modeled output geospatial data layers provided estimated annual loads and yields of nitrate from groundwater into streams. The spatial distribution of annual nitrate yields from groundwater estimated by this method was compared to the total watershed yields of all sources estimated from a Chesapeake Bay SPAtially Referenced Regressions On Watershed attributes (SPARROW) water-quality model. The comparison showed similar spatial patterns. The regression model for groundwater contribution had similar but lower yields, suggesting that groundwater is an important source of nitrogen for streams in the Chesapeake Bay watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175160","usgsCitation":"Terziotti, Silvia, Capel, P.D., Tesoriero, A.J., Hopple, J.A., and Kronholm, S.C., 2017, Estimates of nitrate loads and yields from groundwater to streams in the Chesapeake Bay watershed based on land use and geology: U.S. Geological Survey Scientific Investigations Report 2017–5160, 20 p., https://doi.org/10.3133/sir20175160.","productDescription":"Report: iv, 20 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086227","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":352267,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79G5KP4","text":"USGS data release","description":"USGS data release","linkHelpText":"Datasets and metadata for estimates of nitrate loads and yields from groundwater to streams in the 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This report is a user guide for the low-flow analysis methods provided with version 1.0 of the Surface Water Toolbox (SWToolbox) computer program. The software combines functionality from two software programs—U.S. Geological Survey (USGS) SWSTAT and U.S. Environmental Protection Agency (EPA) DFLOW. Both of these programs have been used primarily for computation of critical low-flow statistics. The main analysis methods are the computation of hydrologic frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10), computation of design flows including biologically based flows, and computation of flow-duration curves and duration hydrographs. Other annual, monthly, and seasonal statistics can also be computed. The interface facilitates retrieval of streamflow discharge data from the USGS National Water Information System and outputs text reports for a record of the analysis. Tools for graphing data and screening tests are available to assist the analyst in conducting the analysis.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Statistical analysis in Book 4: Hydrologic analysis and interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4A11","collaboration":"Prepared in cooperation with the U.S. Environment Protection Agency","usgsCitation":"Kiang, J.E., Flynn, K.M., Zhai, Tong, Hummel, Paul, and Granato, Gregory, 2018, SWToolbox: A surface-water tool-box for statistical analysis of streamflow time series: U.S. Geological Survey Techniques and Methods, book 4, chap. A–11, 33 p., https://doi.org/10.3133/tm4A11.","productDescription":"Report: vii, 34 p.; Software Download and Release Notes","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086817","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":352238,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/a11/coverthb.jpg"},{"id":352239,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/a11/tm4a11.pdf","text":"Report","size":"5.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-A11"},{"id":352240,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://water.usgs.gov/osw/swtoolbox/","text":"Software Download and Release Notes","linkHelpText":"- SWToolbox Software Information"}],"publicComments":"This report is Chapter 11 of Section A: Statistical analysis in Book 4: Hydrologic analysis and interpretation.","contact":"Chief, Analysis and Prediction Branch
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Nitrate contamination of groundwater in agricultural areas poses a major challenge to the sustainability of water resources. Aquifer vulnerability models are useful tools that can help resource managers identify areas of concern, but quantifying nitrogen (N) inputs in such models is challenging, especially at large spatial scales. We sought to improve regional nitrate (NO3−) input functions by characterizing unsaturated zone NO3− transport to groundwater through use of surrogate, machine-learning metamodels of a process-based N flux model. The metamodels used boosted regression trees (BRTs) to relate mappable landscape variables to parameters and outputs of a previous “vertical flux method” (VFM) applied at sampled wells in the Fox, Wolf, and Peshtigo (FWP) river basins in northeastern Wisconsin. In this context, the metamodels upscaled the VFM results throughout the region, and the VFM parameters and outputs are the metamodel response variables. The study area encompassed the domain of a detailed numerical model that provided additional predictor variables, including groundwater recharge, to the metamodels. We used a statistical learning framework to test a range of model complexities to identify suitable hyperparameters of the six BRT metamodels corresponding to each response variable of interest: NO3− source concentration factor (which determines the local NO3− input concentration); unsaturated zone travel time; NO3− concentration at the water table in 1980, 2000, and 2020 (three separate metamodels); and NO3− “extinction depth”, the eventual steady state depth of the NO3−front. The final metamodels were trained to 129 wells within the active numerical flow model area, and considered 58 mappable predictor variables compiled in a geographic information system (GIS). These metamodels had training and cross-validation testing R2 values of 0.52 – 0.86 and 0.22 – 0.38, respectively, and predictions were compiled as maps of the above response variables. Testing performance was reasonable, considering that we limited the metamodel predictor variables to mappable factors as opposed to using all available VFM input variables. Relationships between metamodel predictor variables and mapped outputs were generally consistent with expectations, e.g. with greater source concentrations and NO3− at the groundwater table in areas of intensive crop use and well drained soils. Shorter unsaturated zone travel times in poorly drained areas likely indicated preferential flow through clay soils, and a tendency for fine grained deposits to collocate with areas of shallower water table. Numerical estimates of groundwater recharge were important in the metamodels and may have been a proxy for N input and redox conditions in the northern FWP, which had shallow predicted NO3− extinction depth. The metamodel results provide proof-of-concept for regional characterization of unsaturated zone NO3− transport processes in a statistical framework based on readily mappable GIS input variables.
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2018 - Improving predictions of hydrological low-flow indices in ungaged basins using machine learning","interactions":[],"lastModifiedDate":"2018-07-17T09:55:03","indexId":"70198127","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Improving predictions of hydrological low-flow indices in ungaged basins using machine learning","docAbstract":"We compare the ability of eight machine-learning models (elastic net, gradient boosting, kernel-k-nearest neighbors, two variants of support vector machines, M5-cubist, random forest, and a meta-learning ensemble M5-cubist model) and four baseline models (ordinary kriging, a unit area discharge model, and two variants of censored regression) to generate estimates of the annual minimum 7-day mean streamflow with an annual exceedance probability of 90% (7Q10) at 224 unregulated sites in South Carolina, Georgia, and Alabama, USA. The machine-learning models produced substantially lower cross validation errors compared to the baseline models. The meta-learning M5-cubist model had the lowest root-mean-squared-error of 26.72 cubic feet per second. Partial dependence plots show that 7Q10s are likely moderated by late summer and early fall precipitation and the infiltration capacity of basin soils.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2017.12.021","usgsCitation":"Worland, S.C., Farmer, W.H., and Kiang, J.E., 2018, Improving predictions of hydrological low-flow indices in ungaged basins using machine learning: Environmental Modelling and Software, v. 101, p. 169-182, https://doi.org/10.1016/j.envsoft.2017.12.021.","productDescription":"14 p.","startPage":"169","endPage":"182","ipdsId":"IP-080308","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":468958,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2017.12.021","text":"Publisher Index Page"},{"id":437995,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7CR5S4T","text":"USGS data release","linkHelpText":"7Q10 records and basin characteristics for 224 basins in South Carolina, Georgia, and Alabama 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wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":4374,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","middleInitial":"H.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":740155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiang, Julie E. 0000-0003-0653-4225 jkiang@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-4225","contributorId":2179,"corporation":false,"usgs":true,"family":"Kiang","given":"Julie","email":"jkiang@usgs.gov","middleInitial":"E.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":740156,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70197461,"text":"70197461 - 2018 - Species‐ and habitat‐specific otolith chemistry patterns inform riverine fisheries management","interactions":[],"lastModifiedDate":"2018-06-06T11:28:19","indexId":"70197461","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Species‐ and habitat‐specific otolith chemistry patterns inform riverine fisheries management","docAbstract":"Geology and hydrology are drivers of water chemistry and thus important considerations for fish otolith chemistry research. However, other factors such as species and habitat identity may have predictive ability, enabling selection of appropriate elemental signatures prior to costly, perhaps unnecessary water/age‐0 fish sampling. The goal of this study was to develop a predictive methodology for using species and habitat identity to design efficient otolith chemistry studies. Duplicate water samples and age‐0 fish were collected from 61 sites in 4 Missouri River reservoirs for walleye Sander vitreus and one impoundment (Lake Sharpe, South Dakota) for other fishes (bluegill Lepomis macrochirus, black crappie Pomoxis nigromaculatus, gizzard shad Dorosoma cepedianum, largemouth bass Micropterus salmoides, smallmouth bass M. dolomieu, white bass Morone chrysops, white crappie P. annularis, and yellow perch Perca flavescens). Water chemistry (barium:calcium [Ba:Ca], strontium:calcium [Sr:Ca]) was temporally stable, spatially variable, and highly correlated with otolith chemistry for all species except yellow perch. Classification accuracies based on bivariate Ba:Ca and Sr:Ca signatures were high (84% across species) yet varied between floodplain and main‐channel habitats in a species‐specific manner. Thus, to maximize the reliability of otolith chemistry, researchers can use species classifications presented herein to inform habitat selection (e.g., study reservoir‐oriented species such as white bass in main‐channel environments) and habitat‐based classifications to inform species selection (e.g., focus floodplain studies on littoral species such as largemouth bass). Overall, species and habitat identity are important considerations for efficient, effective otolith chemistry studies that inform and advance fisheries and aquatic resource management.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3248","usgsCitation":"Radigan, W., Carlson, A.K., Kientz, J., Chipps, S.R., Fincel, M.J., and Graeb, B.D., 2018, Species‐ and habitat‐specific otolith chemistry patterns inform riverine fisheries management: River Research and Applications, v. 34, no. 3, p. 279-287, https://doi.org/10.1002/rra.3248.","productDescription":"9 p.","startPage":"279","endPage":"287","ipdsId":"IP-092404","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":354760,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota, South Dakota","otherGeospatial":"Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.27,\n 42.49\n ],\n [\n -96.45,\n 42.49\n ],\n [\n -96.45,\n 47.39\n ],\n [\n -103.27,\n 47.39\n ],\n [\n -103.27,\n 42.49\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-30","publicationStatus":"PW","scienceBaseUri":"5b46e5ade4b060350a15d200","contributors":{"authors":[{"text":"Radigan, William","contributorId":205424,"corporation":false,"usgs":false,"family":"Radigan","given":"William","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":737249,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carlson, Andrew K.","contributorId":172103,"corporation":false,"usgs":false,"family":"Carlson","given":"Andrew","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":737250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kientz, Jeremy","contributorId":205425,"corporation":false,"usgs":false,"family":"Kientz","given":"Jeremy","email":"","affiliations":[{"id":37104,"text":"South Dakota Department of Game, Fish and Parks","active":true,"usgs":false}],"preferred":false,"id":737251,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chipps, Steven R. 0000-0001-6511-7582 steve_chipps@usgs.gov","orcid":"https://orcid.org/0000-0001-6511-7582","contributorId":2243,"corporation":false,"usgs":true,"family":"Chipps","given":"Steven","email":"steve_chipps@usgs.gov","middleInitial":"R.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":737248,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fincel, Mark J.","contributorId":171853,"corporation":false,"usgs":false,"family":"Fincel","given":"Mark","email":"","middleInitial":"J.","affiliations":[{"id":26957,"text":"South Dakota Game, Fish and Parks, Ft. 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S.","affiliations":[{"id":26956,"text":"Departement of Natural Resource Management, Brookings, SD","active":true,"usgs":false}],"preferred":false,"id":737253,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196029,"text":"70196029 - 2018 - Potential effects of elevated base flow and midsummer spike flow experiments on riparian vegetation along the Green River","interactions":[],"lastModifiedDate":"2018-03-15T10:08:22","indexId":"70196029","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NRSS/WRD/NRR—2018/1603","title":"Potential effects of elevated base flow and midsummer spike flow experiments on riparian vegetation along the Green River","docAbstract":"The Upper Colorado River Endangered Fish Recovery Program has requested experimental flow releases from Flaming Gorge Dam for (1) elevated summer base flows to promote larval endangered Colorado pikeminnow, and (2) midsummer spike flows to disadvantage spawning invasive smallmouth bass. This white paper explores the effects of these proposed flow modifications on riparian vegetation and sediment deposition downstream along the Green River. Although modest in magnitude, the elevated base flows and possible associated reductions in magnitude or duration of peak flows would exacerbate a long-term trend of flow stabilization on the Green River that is already leading to proliferation of vegetation including invasive tamarisk along the channel and associated sediment deposition, channel narrowing and channel simplification. Midsummer spike flows could promote establishment of late-flowering plants like tamarisk. Because channel narrowing and simplification threaten persistence and quality of backwater and side channel features needed by endangered fish, the proposed flow modifications could lead to degradation of fish habitat. Channel narrowing and vegetation encroachment could be countered by increases in peak flows or reductions in base flows in some years and by prescription of rapid flow declines following midsummer spike flows. These strategies for reducing vegetation encroachment would need to be balanced with flow\nneeds of other riverine resources. Use of high flows to remove unwanted vegetation is constrained by current operational guidance for Flaming Gorge Dam, which attempts to limit spills (i.e., flows greater than 8600 ft3 /s) that might contribute to cavitation and lead to dam safety concerns. Therefore, reversing vegetation encroachment is more likely to succeed if implemented while plants are still small. Annual monitoring of near-channel vegetation and topography would enable managers to prescribe a timely hydrologic response in case the proposed flow experiments lead to vegetation encroachment and habitat degradation.","language":"English","publisher":"National Park Service","usgsCitation":"Friedman, J.M., 2018, Potential effects of elevated base flow and midsummer spike flow experiments on riparian vegetation along the Green River: Natural Resource Report NPS/NRSS/WRD/NRR—2018/1603, vi, 19 p.","productDescription":"vi, 19 p.","ipdsId":"IP-090611","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":352501,"type":{"id":15,"text":"Index Page"},"url":"https://irma.nps.gov/DataStore/Reference/Profile/2252016"},{"id":352542,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Green River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.05529785156249,\n 38.190704293996504\n ],\n [\n -108.15490722656249,\n 38.190704293996504\n ],\n [\n -108.15490722656249,\n 41.253032440653186\n ],\n [\n -111.05529785156249,\n 41.253032440653186\n ],\n [\n -111.05529785156249,\n 38.190704293996504\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee712e4b0da30c1bfc0c6","contributors":{"authors":[{"text":"Friedman, Jonathan M. 0000-0002-1329-0663 friedmanj@usgs.gov","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":2473,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","email":"friedmanj@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":731064,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70196265,"text":"70196265 - 2018 - Technical note: False low turbidity readings from optical probes during high suspended-sediment concentrations","interactions":[],"lastModifiedDate":"2018-03-29T10:32:21","indexId":"70196265","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1928,"text":"Hydrology and Earth System Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Technical note: False low turbidity readings from optical probes during high suspended-sediment concentrations","docAbstract":"Turbidity, a measure of water clarity, is monitored for a variety of purposes including (1) to help determine whether water is safe to drink, (2) to establish background conditions of lakes and rivers and detect pollution caused by construction projects and stormwater discharge, (3) to study sediment transport in rivers and erosion in catchments, (4) to manage siltation of water reservoirs, and (5) to establish connections with aquatic biological properties, such as primary production and predator–prey interactions. Turbidity is typically measured with an optical probe that detects light scattered from particles in the water. Probes have defined upper limits of the range of turbidity that they can measure. The general assumption is that when turbidity exceeds this upper limit, the values of turbidity will be constant, i.e., the probe is pegged
; however, this assumption is not necessarily valid. In rivers with limited variation in the physical properties of the suspended sediment, at lower suspended-sediment concentrations, an increase in suspended-sediment concentration will cause a linear increase in turbidity. When the suspended-sediment concentration in these rivers is high, turbidity levels can exceed the upper measurement limit of an optical probe and record a constant pegged
value. However, at extremely high suspended-sediment concentrations, optical turbidity probes do not necessarily stay pegged
at a constant value. Data from the Colorado River in Grand Canyon, Arizona, USA, and a laboratory experiment both demonstrate that when turbidity exceeds instrument-pegged conditions, increasing suspended-sediment concentration (and thus increasing turbidity) may cause optical probes to record decreasing false
turbidity values that appear to be within the valid measurement range of the probe. Therefore, under high-turbidity conditions, other surrogate measurements of turbidity (e.g., acoustic-attenuation measurements or suspended-sediment samples) are necessary to correct these low false turbidity measurements and accurately measure turbidity.
Digital flood-inundation maps for a 1.9-mile reach of Cedar Creek at Auburn, Indiana (Ind.), from the First Street bridge, downstream to the streamgage at 18th Street, then ending approximately 1,100 feet (ft) downstream of the Baltimore and Ohio railroad, were created by the U.S. Geological Survey (USGS) in cooperation with the Indiana Department of Transportation. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science web site at https://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on Cedar Creek at 18th Street at Auburn, Ind. (station number 04179520). Near-real-time stages at this streamgage may be obtained from the USGS National Water Information System at https://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/, although forecasts of flood hydrographs are not available at this site (ABBI3).
Flood profiles were computed for the stream reach by means of a one-dimensional step-backwater model. The hydraulic model was calibrated by using the most current stage-discharge relation at the Cedar Creek at 18th Street at Auburn, Ind. streamgage and the documented high-water marks from the flood of March 11, 2009. The calibrated hydraulic model was then used to compute seven water-surface profiles for flood stages referenced to the streamgage datum and ranging from 7 ft, or near bankfull, to 13 ft, in 1-foot increments. The simulated water-surface profiles were then combined with a geographic information system digital elevation model (derived from light detection and ranging [lidar] data having a 0.98-ft vertical accuracy and 4.9-ft horizontal resolution) to delineate the area flooded at each water level.
The availability of these maps, along with internet information regarding current stage from the USGS streamgage at Cedar Creek at 18th Street at Auburn, Ind., and stream information from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175156","collaboration":"Prepared in cooperation with the Indiana Department of Transportation","usgsCitation":"Fowler, K.K., 2018, Flood-inundation maps for Cedar Creek at 18th Street at Auburn, Indiana: U.S. Geological Survey Scientific Investigations Report 2017–5156, 10 p., https://doi.org/10.3133/sir20175156.","productDescription":"Report: iv, 10 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087585","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":349964,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5156/coverthb.jpg"},{"id":351891,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5156/sir20175156.pdf","text":"Report","size":"6.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5156"},{"id":351892,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72806GR","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial Datasets and Surface-Water Hydraulic Model for Cedar Creek at Auburn, Indiana, Flood-inundation Study "}],"country":"United States","state":"Indiana","city":"Auburn","otherGeospatial":"Cedar Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.0667,\n 41.34582213380196\n ],\n [\n -85.0417,\n 41.34582213380196\n ],\n [\n -85.0417,\n 41.37057703323999\n ],\n [\n -85.0667,\n 41.37057703323999\n ],\n [\n -85.0667,\n 41.34582213380196\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
U.S. Geological Survey
5957 Lakeside Boulevard
Indianapolis, IN 46278-1996
Spatial and temporal variability in the frequency, duration, and severity of hydrological droughts across the conterminous United States (CONUS) was examined using monthly mean streamflow measured at 872 sites from 1951 through 2014. Hydrological drought is identified as starting when streamflow falls below the 20th percentile streamflow value for 3 consecutive months and ending when streamflow remains above the 20th percentile streamflow value for 3 consecutive months. Mean drought frequency for all aggregated ecoregions in CONUS is 16 droughts per 100 years. Mean drought duration is 5 months, and mean drought severity is 39 percent on a scale ranging from 0 percent to 100 percent (with 100% being the most severe). Hydrological drought frequency is highest in the Western Mountains aggregated ecoregion and lowest in the Eastern Highlands, Northeast, and Southeast Plains aggregated ecoregions. Hydrological drought frequencies of 17 or more droughts per 100 years were found for the Central Plains, Southeast Coastal Plains, Western Mountains, and Western Xeric aggregated ecoregions. Drought duration and severity indicate spatial variability among the sites, but unlike drought frequency, do not show coherent spatial patterns. A comparison of an older period (1951–82) with a recent period (1983–2014) indicates few sites have statistically significant changes in drought frequency, drought duration, or drought severity at a 95-percent confidence level.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175099","isbn":"978-1-4113-4205-7","usgsCitation":"Austin, S.H., Wolock, D.M., and Nelms, D.L., 2018, Variability of hydrological droughts in the conterminous United States, 1951 through 2014: U.S. Geological Survey Scientific Investigations Report 2017–5099, 16 p., https://doi.org/10.3133/sir20175099.","productDescription":"Report: v, 16 p.; Data release","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063758","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":351816,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70C4T19","text":"USGS data release","description":"USGS data release","linkHelpText":"Monthly streamflows, drought indices, and supporting statistics for USGS gage stations used to identify variability of hydrological droughts in the conterminous United States, 1951 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\"}}]}\n\n\n","contact":"Director, Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
The U.S. Geological Survey collects groundwater data and conducts studies to monitor hydrologic conditions, define groundwater resources, and address problems related to water supply, water use, and water quality. In Georgia, water levels were monitored continuously at 157 wells during calendar years 2015 and 2016. Because of missing data or short periods of record (less than 5 years) for several of these wells, data for 147 wells are presented in this report. These wells include 15 in the surficial aquifer system, 18 in the Brunswick aquifer system and equivalent sediments, 59 in the Upper Floridan aquifer, 13 in the Lower Floridan aquifer and underlying units, 9 in the Claiborne aquifer, 1 in the Gordon aquifer, 8 in the Clayton aquifer, 16 in the Cretaceous aquifer system, 2 in Paleozoic-rock aquifers, and 6 in crystalline-rock aquifers. Data from the well network indicate that water levels generally rose during the 10-year period from 2007 through 2016, with water levels rising in 105 wells and declining in 31 wells; insufficient data prevented determination of a 10-year trend in 11 wells. Water levels declined over the long-term period of record at 80 wells, increased at 62 wells, and remained relatively constant at 5 wells.
In addition to continuous water-level data, periodic water-level data were collected and used to construct potentiometric-surface maps for the Upper Floridan aquifer in the Brunswick–Glynn County area during October 2015 and October 2016 and in the Albany–Dougherty County area during December 2015 and November and December 2016. Periodic water-level measurements were also collected and used to construct potentiometric-surface maps for the Cretaceous aquifer system in the Augusta–Richmond County area during July 2015 and June 2016. In general, water levels in the Upper Floridan aquifer were higher during 2015 than during 2016 in the Brunswick–Glynn County and Albany–Dougherty County areas due to higher precipitation during 2015. Water levels were lower, however, during 2015 than during 2016 in the Cretaceous aquifer system in the Augusta–Richmond County area.
In the Brunswick area, maps showing the chloride concentration of water in the Upper Floridan aquifer constructed using data collected from 33 wells during October 2015 and from 30 wells during October 2016 indicate that chloride concentrations remained above the U.S. Environmental Protection Agency’s secondary drinking-water standard in an approximately 2-square-mile area. During calendar years 2015–16, chloride concentrations generally were similar to those measured during 2012–14; however, some wells did show an increase in chloride concentration, likely due to increases in pumping.
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\"}}]}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
Rainfall-induced shallow landsliding is a persistent hazard to human life and property. Despite the observed connection between infiltration through the unsaturated zone and shallow landslide initiation, there is considerable uncertainty in how estimates of unsaturated soil-water retention properties affect slope stability assessment. This source of uncertainty is critical to evaluating the utility of physics-based hydrologic modeling as a tool for landslide early warning. We employ a numerical model of variably saturated groundwater flow parameterized with an ensemble of texture-, laboratory-, and field-based estimates of soil-water retention properties for an extensively monitored landslide-prone site in the San Francisco Bay Area, CA, USA. Simulations of soil-water content, pore-water pressure, and the resultant factor of safety show considerable variability across and within these different parameter estimation techniques. In particular, we demonstrate that with the same permeability structure imposed across all simulations, the variability in soil-water retention properties strongly influences predictions of positive pore-water pressure coincident with widespread shallow landsliding. We also find that the ensemble of soil-water retention properties imposes an order-of-magnitude and nearly two-fold variability in seasonal and event-scale landslide susceptibility, respectively. Despite the reduced factor of safety uncertainty during wet conditions, parameters that control the dry end of the soil-water retention function markedly impact the ability of a hydrologic model to capture soil-water content dynamics observed in the field. These results suggest that variability in soil-water retention properties should be considered for objective physics-based simulation of landslide early warning criteria.
","language":"English","publisher":"Springer","doi":"10.1007/s10346-018-0950-z","usgsCitation":"Thomas, M.A., Mirus, B.B., Collins, B.D., Lu, N., and Godt, J.W., 2018, Variability in soil-water retention properties and implications for physics-based simulation of landslide early warning criteria: Landslides, v. 15, no. 7, p. 1265-1277, https://doi.org/10.1007/s10346-018-0950-z.","productDescription":"13 p.","startPage":"1265","endPage":"1277","ipdsId":"IP-089282","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":438007,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7M0449D","text":"USGS data release","linkHelpText":"Field data used to support hydrologic modeling for the U.S. Geological Survey's San Francisco Bay Area "BALT1" landslide monitoring site"},{"id":351832,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-12","publicationStatus":"PW","scienceBaseUri":"5afee728e4b0da30c1bfc14a","contributors":{"authors":[{"text":"Thomas, Matthew A.","contributorId":138657,"corporation":false,"usgs":false,"family":"Thomas","given":"Matthew","email":"","middleInitial":"A.","affiliations":[{"id":12482,"text":"Department of Geological and Environmental Sciences, Stanford University, 450 Serra Mall, Building 320, Stanford, California 94305-2115, USA","active":true,"usgs":false}],"preferred":false,"id":729027,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X bbmirus@usgs.gov","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":4064,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin","email":"bbmirus@usgs.gov","middleInitial":"B.","affiliations":[{"id":5077,"text":"Northwest Regional Director's Office","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":729028,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Brian D. bcollins@usgs.gov","contributorId":2406,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":729029,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lu, Ning","contributorId":191360,"corporation":false,"usgs":false,"family":"Lu","given":"Ning","email":"","affiliations":[{"id":12620,"text":"U.S. Army Corp. of Engineers","active":true,"usgs":false}],"preferred":false,"id":729030,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Godt, Jonathan W. 0000-0002-8737-2493 jgodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8737-2493","contributorId":1166,"corporation":false,"usgs":true,"family":"Godt","given":"Jonathan","email":"jgodt@usgs.gov","middleInitial":"W.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":729031,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195477,"text":"70195477 - 2018 - Clayey landslide initiation and acceleration strongly modulated by soil swelling","interactions":[],"lastModifiedDate":"2018-03-19T11:10:31","indexId":"70195477","displayToPublicDate":"2018-02-20T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Clayey landslide initiation and acceleration strongly modulated by soil swelling","docAbstract":"Largely unknown mechanisms restrain motion of clay-rich, slow-moving landslides that are widespread worldwide and rarely accelerate catastrophically. We studied a clayey, slow-moving landslide typical of thousands in northern California, USA, to decipher hydrologic-mechanical interactions that modulate landslide dynamics. Similar to some other studies, observed pore-water pressures correlated poorly with landslide reactivation and speed. In situ and laboratory measurements strongly suggested that variable pressure along the landslide's lateral shear boundaries resulting from seasonal soil expansion and contraction modulated its reactivation and speed. Slope-stability modeling suggested that the landslide's observed behavior could be predicted by including transient swell pressure as a resistance term, whereas modeling considering only transient hydrologic conditions predicted movement 5–6 months prior to when it was observed. All clayey soils swell to some degree; hence, our findings suggest that swell pressure likely modulates motion of many landslides and should be considered to improve forecasts of clayey landslide initiation and mobility.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017GL076807","usgsCitation":"Schulz, W.H., Smith, J.B., Wang, G., Jiang, Y., and Roering, J., 2018, Clayey landslide initiation and acceleration strongly modulated by soil swelling: Geophysical Research Letters, v. 45, no. 4, p. 1888-1896, https://doi.org/10.1002/2017GL076807.","productDescription":"9 p.","startPage":"1888","endPage":"1896","ipdsId":"IP-093100","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":468987,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl076807","text":"Publisher Index Page"},{"id":438006,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GF0SFS","text":"USGS data release","linkHelpText":"Data from in-situ landslide monitoring, Trinity County, California"},{"id":351813,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"45","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5afee72ae4b0da30c1bfc15c","contributors":{"authors":[{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":728780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Joel B. 0000-0001-7219-7875 jbsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-7219-7875","contributorId":4925,"corporation":false,"usgs":true,"family":"Smith","given":"Joel","email":"jbsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":728781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wang, Gonghui","contributorId":202546,"corporation":false,"usgs":false,"family":"Wang","given":"Gonghui","email":"","affiliations":[{"id":36476,"text":"Disaster Prevention Research Institute, Kyoto University","active":true,"usgs":false}],"preferred":false,"id":728782,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jiang, Yao","contributorId":202547,"corporation":false,"usgs":false,"family":"Jiang","given":"Yao","email":"","affiliations":[{"id":36476,"text":"Disaster Prevention Research Institute, Kyoto University","active":true,"usgs":false}],"preferred":false,"id":728783,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roering, Joshua J.","contributorId":194297,"corporation":false,"usgs":false,"family":"Roering","given":"Joshua J.","affiliations":[],"preferred":false,"id":728784,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70199666,"text":"70199666 - 2018 - International Limnogeology Congress (ILIC6), Reno USA, special issue on new limnogeological research focused on Holocene lake systems","interactions":[],"lastModifiedDate":"2018-11-21T14:10:04","indexId":"70199666","displayToPublicDate":"2018-02-15T14:08:34","publicationYear":"2018","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"title":"International Limnogeology Congress (ILIC6), Reno USA, special issue on new limnogeological research focused on Holocene lake systems","docAbstract":"The 6th International Limnogeology Congress (ILIC6) of the International Association of Limnogeology (IAL) was held in Reno, Nevada, USA 15–19 June, 2015. The successful congress brought together a wide variety of academic, government, and industry participants from 20 countries and six continents. The highpoint of the four-day meeting were eight keynote addresses highlighting cutting-edge research in paleolimnology and limnogeology. These addresses were often the focal point of discussions throughout the congress. Among the topics covered in the more than 140 poster presentations included the use of biological proxies to identify paleoenvironmental change due to natural variability and human impact, the evolution of mineral formation in saline lakes, sedimentation deposition processes in Mesoproterozoic lakes, the role of groundwater in lacustrine hydrology, and organic and inorganic contaminant histories in modern lakes.","largerWorkTitle":"Journal of Paleolimnology","conferenceTitle":"International Limnogeology Congress (ILIC6)","conferenceDate":"June 15-19, 2015","conferenceLocation":"Reno, Nevada, USA","language":"English","publisher":"Springer","doi":"10.1007/s10933-017-0003-y","usgsCitation":"Starratt, S.W., and Rosen, M.R., 2018, International Limnogeology Congress (ILIC6), Reno USA, special issue on new limnogeological research focused on Holocene lake systems, in Journal of Paleolimnology, v. 59, no. 2, Reno, Nevada, USA, June 15-19, 2015, p. 135-138, https://doi.org/10.1007/s10933-017-0003-y.","productDescription":"4 p.","startPage":"135","endPage":"138","ipdsId":"IP-090179","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":359641,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bf67cf5e4b045bfcae2d000","contributors":{"authors":[{"text":"Starratt, Scott W. 0000-0001-9405-1746 sstarrat@usgs.gov","orcid":"https://orcid.org/0000-0001-9405-1746","contributorId":2891,"corporation":false,"usgs":true,"family":"Starratt","given":"Scott","email":"sstarrat@usgs.gov","middleInitial":"W.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":746122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosen, Michael R. 0000-0003-3991-0522 mrosen@usgs.gov","orcid":"https://orcid.org/0000-0003-3991-0522","contributorId":495,"corporation":false,"usgs":true,"family":"Rosen","given":"Michael","email":"mrosen@usgs.gov","middleInitial":"R.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":746123,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70195806,"text":"70195806 - 2018 - Modelling surface-water depression storage in a Prairie Pothole Region","interactions":[],"lastModifiedDate":"2018-03-02T11:12:22","indexId":"70195806","displayToPublicDate":"2018-02-15T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Modelling surface-water depression storage in a Prairie Pothole Region","docAbstract":"In this study, the Precipitation-Runoff Modelling System (PRMS) was used to simulate changes in surface-water depression storage in the 1,126-km2 Upper Pipestem Creek basin located within the Prairie Pothole Region of North Dakota, USA. The Prairie Pothole Region is characterized by millions of small water bodies (or surface-water depressions) that provide numerous ecosystem services and are considered an important contribution to the hydrologic cycle. The Upper Pipestem PRMS model was extracted from the U.S. Geological Survey's (USGS) National Hydrologic Model (NHM), developed to support consistent hydrologic modelling across the conterminous United States. The Geospatial Fabric database, created for the USGS NHM, contains hydrologic model parameter values derived from datasets that characterize the physical features of the entire conterminous United States for 109,951 hydrologic response units. Each hydrologic response unit in the Geospatial Fabric was parameterized using aggregated surface-water depression area derived from the National Hydrography Dataset Plus, an integrated suite of application-ready geospatial datasets. This paper presents a calibration strategy for the Upper Pipestem PRMS model that uses normalized lake elevation measurements to calibrate the parameters influencing simulated fractional surface-water depression storage. Results indicate that inclusion of measurements that give an indication of the change in surface-water depression storage in the calibration procedure resulted in accurate changes in surface-water depression storage in the water balance. Regionalized parameterization of the USGS NHM will require a proxy for change in surface-storage to accurately parameterize surface-water depression storage within the USGS NHM.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11416","usgsCitation":"Hay, L.E., Norton, P.A., Viger, R.J., Markstrom, S.L., Regan, R.S., and Vanderhoof, M.K., 2018, Modelling surface-water depression storage in a Prairie Pothole Region: Hydrological Processes, v. 32, no. 4, p. 462-479, https://doi.org/10.1002/hyp.11416.","productDescription":"18 p.","startPage":"462","endPage":"479","ipdsId":"IP-080013","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":352175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Upper Pipestem Creek basin","volume":"32","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-08","publicationStatus":"PW","scienceBaseUri":"5afee72de4b0da30c1bfc182","contributors":{"authors":[{"text":"Hay, Lauren E. 0000-0003-3763-4595 lhay@usgs.gov","orcid":"https://orcid.org/0000-0003-3763-4595","contributorId":1287,"corporation":false,"usgs":true,"family":"Hay","given":"Lauren","email":"lhay@usgs.gov","middleInitial":"E.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":729974,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Norton, Parker A. 0000-0002-4638-2601 pnorton@usgs.gov","orcid":"https://orcid.org/0000-0002-4638-2601","contributorId":2257,"corporation":false,"usgs":true,"family":"Norton","given":"Parker","email":"pnorton@usgs.gov","middleInitial":"A.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":729978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Viger, Roland J. 0000-0003-2520-714X rviger@usgs.gov","orcid":"https://orcid.org/0000-0003-2520-714X","contributorId":168799,"corporation":false,"usgs":true,"family":"Viger","given":"Roland","email":"rviger@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":729976,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Markstrom, Steven L. 0000-0001-7630-9547 markstro@usgs.gov","orcid":"https://orcid.org/0000-0001-7630-9547","contributorId":146553,"corporation":false,"usgs":true,"family":"Markstrom","given":"Steven","email":"markstro@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":729977,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Regan, R. Steve 0000-0003-4803-8596 rsregan@usgs.gov","orcid":"https://orcid.org/0000-0003-4803-8596","contributorId":196973,"corporation":false,"usgs":true,"family":"Regan","given":"R.","email":"rsregan@usgs.gov","middleInitial":"Steve","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":729975,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":729979,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191170,"text":"sir20175108 - 2018 - Stream-channel and watershed delineations and basin-characteristic measurements using lidar elevation data for small drainage basins within the Des Moines Lobe landform region in Iowa","interactions":[],"lastModifiedDate":"2018-02-14T15:01:18","indexId":"sir20175108","displayToPublicDate":"2018-02-14T13:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5108","title":"Stream-channel and watershed delineations and basin-characteristic measurements using lidar elevation data for small drainage basins within the Des Moines Lobe landform region in Iowa","docAbstract":"Basin-characteristic measurements related to stream length, stream slope, stream density, and stream order have been identified as significant variables for estimation of flood, flow-duration, and low-flow discharges in Iowa. The placement of channel initiation points, however, has always been a matter of individual interpretation, leading to differences in stream definitions between analysts.
This study investigated five different methods to define stream initiation using 3-meter light detection and ranging (lidar) digital elevation models (DEMs) data for 17 streamgages with drainage areas less than 50 square miles within the Des Moines Lobe landform region in north-central Iowa. Each DEM was hydrologically enforced and the five stream initiation methods were used to define channel initiation points and the downstream flow paths. The five different methods to define stream initiation were tested side-by-side for three watershed delineations: (1) the total drainage-area delineation, (2) an effective drainage-area delineation of basins based on a 2-percent annual exceedance probability (AEP) 12-hour rainfall, and (3) an effective drainage-area delineation based on a 20-percent AEP 12-hour rainfall.
Generalized least squares regression analysis was used to develop a set of equations for sites in the Des Moines Lobe landform region for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEPs. A total of 17 streamgages were included in the development of the regression equations. In addition, geographic information system software was used to measure 58 selected basin-characteristics for each streamgage.
Results of the regression analyses of the 15 lidar datasets indicate that the datasets that produce regional regression equations (RREs) with the best overall predictive accuracy are the National Hydrographic Dataset, Iowa Department of Natural Resources, and profile curvature of 0.5 stream initiation methods combined with the 20-percent AEP 12-hour rainfall watershed delineation method. These RREs have a mean average standard error of prediction (SEP) for 4-, 2-, and 1-percent AEP discharges of 53.9 percent and a mean SEP for all eight AEPs of 55.5 percent. Compared to the RREs developed in this study using the basin characteristics from the U.S. Geological Survey StreamStats application, the lidar basin characteristics provide better overall predictive accuracy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175108","collaboration":"Prepared in cooperation with the Iowa Department of Transportation and the Iowa Highway Research Board (Project TR–692) ","usgsCitation":"Eash, D.A., Barnes, K.K., O’Shea, P.S., and Gelder, B.K., 2018, Stream-channel and watershed delineations and basin-characteristic measurements using lidar elevation data for small drainage basins within the Des Moines Lobe landform region in Iowa: U.S. Geological Survey Scientific Investigations Report 2017–5108, 23 p., https://doi.org/10.3133/sir20175108. ","productDescription":"vi, 23 p.","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-081688","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"links":[{"id":351551,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5108/coverthb.jpg"},{"id":351552,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5108/sir20175108.pdf","text":"Report","size":"1.48 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5108"}],"country":"United States","state":"Iowa","otherGeospatial":"Des Moines Lobe landform region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96,\n 41.5\n ],\n [\n -93,\n 41.5\n ],\n [\n -93,\n 43.50075243569041\n ],\n [\n -96,\n 43.50075243569041\n ],\n [\n -96,\n 41.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
400 S. Clinton St., Rm 269
Iowa City, IA 52240
1.Dams disrupt the river continuum, altering hydrology, biodiversity, and energy flow. Although research indicates that tributary inputs have the potential to dilute these effects, knowledge at the food web level is still scarce.
2.Here we examined the riverine food web structure of the Colorado River below Glen Canyon Dam, focusing on organic matter sources, trophic diversity, and food chain length. We asked how these components respond to pulsed flows from tributaries following monsoon thunderstorms that seasonally increase streamflow in the American Southwest.
3.Tributaries increased the relative importance of terrestrial organic matter, particularly during the wet season below junctures of key tributaries. This contrasted with the algal-based food web present immediately below Glen Canyon Dam.
4.Tributary inputs during the monsoon also increased trophic diversity and food chain length: food chain length peaked below the confluence with the largest tributary (by discharge) in Grand Canyon, increasing by >1 trophic level over a 4-5 kilometre reach possibly due to aquatic prey being flushed into the mainstem during heavy rain events.
5.Our results illustrate that large tributaries can create seasonal discontinuities, influencing riverine food web structure in terms of allochthony, food web diversity, and food chain length.
6.Synthesis and applications. Pulsed flows from unregulated tributaries following seasonal monsoon rains increase the importance of terrestrially-derived organic matter in large, regulated river food webs, increasing food chain length and trophic diversity downstream of tributary inputs. Protecting unregulated tributaries within hydropower cascades may be important if we are to mitigate food web structure alteration due to flow regulation by large dams. This is critical in the light of global hydropower development, especially in megadiverse, developing countries where dam placement (including completed and planned structures) is in tributaries.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13109","usgsCitation":"Sabo, J., Caron, M., Doucett, R.R., Dibble, K.L., Ruhi, A., Marks, J., Hungate, B., and Kennedy, T.A., 2018, Pulsed flows, tributary inputs, and food web structure in a highly regulated river: Journal of Applied Ecology, v. 55, no. 4, p. 1884-1895, https://doi.org/10.1111/1365-2664.13109.","productDescription":"12 p.","startPage":"1884","endPage":"1895","ipdsId":"IP-069945","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":468998,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13109","text":"Publisher Index Page"},{"id":438013,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FX78CV","text":"USGS data release","linkHelpText":"Food Web Data, Colorado River Corridor, Grand Canyon, Arizona, 2006"},{"id":351612,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado River","volume":"55","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-02","publicationStatus":"PW","scienceBaseUri":"5afee72fe4b0da30c1bfc18e","contributors":{"authors":[{"text":"Sabo, John","contributorId":202486,"corporation":false,"usgs":false,"family":"Sabo","given":"John","affiliations":[{"id":36455,"text":"Julie Ann Wrigley Global Institute of Sustainability","active":true,"usgs":false}],"preferred":false,"id":728553,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caron, Melanie","contributorId":202487,"corporation":false,"usgs":false,"family":"Caron","given":"Melanie","email":"","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":728554,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doucett, Richard R.","contributorId":172996,"corporation":false,"usgs":false,"family":"Doucett","given":"Richard","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":728555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dibble, Kimberly L. 0000-0003-0799-4477 kdibble@usgs.gov","orcid":"https://orcid.org/0000-0003-0799-4477","contributorId":5174,"corporation":false,"usgs":true,"family":"Dibble","given":"Kimberly","email":"kdibble@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":728552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ruhi, Albert","contributorId":202488,"corporation":false,"usgs":false,"family":"Ruhi","given":"Albert","email":"","affiliations":[{"id":36456,"text":"Julie Ann Wrigley Global Institute of Sustainability ASU","active":true,"usgs":false}],"preferred":false,"id":728556,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Marks, Jane","contributorId":202489,"corporation":false,"usgs":false,"family":"Marks","given":"Jane","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":728557,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hungate, Bruce","contributorId":202490,"corporation":false,"usgs":false,"family":"Hungate","given":"Bruce","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":728558,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kennedy, Theodore A. 0000-0003-3477-3629 tkennedy@usgs.gov","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":167537,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","email":"tkennedy@usgs.gov","middleInitial":"A.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":728559,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70195132,"text":"70195132 - 2018 - Modeling drivers of phosphorus loads in Chesapeake Bay tributaries and inferences about long-term change","interactions":[],"lastModifiedDate":"2018-02-08T13:24:12","indexId":"70195132","displayToPublicDate":"2018-02-08T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Modeling drivers of phosphorus loads in Chesapeake Bay tributaries and inferences about long-term change","docAbstract":"Causal attribution of changes in water quality often consists of correlation, qualitative reasoning, listing references to the work of others, or speculation. To better support statements of attribution for water-quality trends, structural equation modeling was used to model the causal factors of total phosphorus loads in the Chesapeake Bay watershed. By transforming, scaling, and standardizing variables, grouping similar sites, grouping some causal factors into latent variable models, and using methods that correct for assumption violations, we developed a structural equation model to show how causal factors interact to produce total phosphorus loads. Climate (in the form of annual total precipitation and the Palmer Hydrologic Drought Index) and anthropogenic inputs are the major drivers of total phosphorus load in the Chesapeake Bay watershed. Increasing runoff due to natural climate variability is offsetting purposeful management actions that are otherwise decreasing phosphorus loading; consequently, management actions may need to be reexamined to achieve target reductions in the face of climate variability.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2017.10.173","usgsCitation":"Ryberg, K.R., Blomquist, J.D., Sprague, L.A., Sekellick, A.J., and Keisman, J.L., 2018, Modeling drivers of phosphorus loads in Chesapeake Bay tributaries and inferences about long-term change: Science of the Total Environment, v. 616–617, p. 1423-1430, https://doi.org/10.1016/j.scitotenv.2017.10.173.","productDescription":"8 p.","startPage":"1423","endPage":"1430","ipdsId":"IP-080391","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":469009,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2017.10.173","text":"Publisher Index Page"},{"id":351357,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay ","volume":"616–617","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a7d6ff8e4b00f54eb24417b","contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":727090,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Blomquist, Joel D. 0000-0002-0140-6534 jdblomqu@usgs.gov","orcid":"https://orcid.org/0000-0002-0140-6534","contributorId":197860,"corporation":false,"usgs":true,"family":"Blomquist","given":"Joel","email":"jdblomqu@usgs.gov","middleInitial":"D.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":727093,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sprague, Lori A. 0000-0003-2832-6662 lsprague@usgs.gov","orcid":"https://orcid.org/0000-0003-2832-6662","contributorId":726,"corporation":false,"usgs":true,"family":"Sprague","given":"Lori","email":"lsprague@usgs.gov","middleInitial":"A.","affiliations":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":727092,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sekellick, Andrew J. 0000-0002-0440-7655 ajsekell@usgs.gov","orcid":"https://orcid.org/0000-0002-0440-7655","contributorId":4125,"corporation":false,"usgs":true,"family":"Sekellick","given":"Andrew","email":"ajsekell@usgs.gov","middleInitial":"J.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":727091,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keisman, Jennifer L. 0000-0001-6808-9193 jkeisman@usgs.gov","orcid":"https://orcid.org/0000-0001-6808-9193","contributorId":198107,"corporation":false,"usgs":true,"family":"Keisman","given":"Jennifer","email":"jkeisman@usgs.gov","middleInitial":"L.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":727094,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195068,"text":"70195068 - 2018 - Comparison of HSPF and PRMS model simulated flows using different temporal and spatial scales in the Black Hills, South Dakota","interactions":[],"lastModifiedDate":"2018-02-08T12:28:05","indexId":"70195068","displayToPublicDate":"2018-02-08T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2341,"text":"Journal of Hydrologic Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of HSPF and PRMS model simulated flows using different temporal and spatial scales in the Black Hills, South Dakota","docAbstract":"The hydrological simulation program Fortran (HSPF) [Hydrological Simulation Program Fortran version 12.2 (Computer software). USEPA, Washington, DC] and the precipitation runoff modeling system (PRMS) [Precipitation Runoff Modeling System version 4.0 (Computer software). USGS, Reston, VA] models are semidistributed, deterministic hydrological tools for simulating the impacts of precipitation, land use, and climate on basin hydrology and streamflow. Both models have been applied independently to many watersheds across the United States. This paper reports the statistical results assessing various temporal (daily, monthly, and annual) and spatial (small versus large watershed) scale biases in HSPF and PRMS simulations using two watersheds in the Black Hills, South Dakota. The Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (
Global-scale studies suggest that dryland ecosystems dominate an increasing trend in the magnitude and interannual variability of the land CO2 sink. However, such model-based analyses are poorly constrained by measured CO2 exchange in open shrublands, which is the most common global land cover type, covering ∼14% of Earth’s surface. Here we evaluate how the amount and seasonal timing of water availability regulate CO2 exchange between shrublands and the atmosphere. We use eddy covariance data from six US sites across the three warm deserts of North America with observed ranges in annual precipitation of ∼100–400mm, annual temperatures of 13–18°C, and records of 2–8 years (33 site-years in total). The Chihuahuan, Sonoran and Mojave Deserts present gradients in both mean annual precipitation and its seasonal distribution between the wet-winter Mojave Desert and the wet-summer Chihuahuan Desert. We found that due to hydrologic losses during the wettest summers in the Sonoran and Chihuahuan Deserts, evapotranspiration (ET) was a better metric than precipitation of water available to drive dryland CO2 exchange. In contrast with recent synthesis studies across diverse dryland biomes, we found that NEP could not be directly predicted from ET due to wintertime decoupling of the relationship between ecosystem respiration (Reco) and gross ecosystem productivity (GEP). Ecosystem water use efficiency (WUE=GEP/ET) did not differ between winter and summer. Carbon use efficiency (CUE=NEP/GEP), however, was greater in winter because Reco returned a smaller fraction of carbon to the atmosphere (23% of GEP) than in summer (77%). Combining the water-carbon relations found here with historical precipitation since 1980, we estimate that lower average winter precipitation during the 21st century reduced the net carbon sink of the three deserts by an average of 6.8TgC yr1. Our results highlight that winter precipitation is critical to the annual carbon balance of these warm desert shrublands.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.agrformet.2017.11.005","usgsCitation":"Biederman, J.A., Scott, R.L., Arnone, J.A., Jasoni, R.L., Litvak, M.E., Moreo, M.T., Papuga, S.A., Ponce-Campos, G.E., Schreiner-McGraw, A.P., and Vivoni, E.R., 2018, Shrubland carbon sink depends upon winter water availability in the warm deserts of North America: Agricultural and Forest Meteorology, v. 249, p. 407-419, https://doi.org/10.1016/j.agrformet.2017.11.005.","productDescription":"13 p.","startPage":"407","endPage":"419","ipdsId":"IP-088519","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":469024,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1549057","text":"Publisher Index Page"},{"id":351309,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"249","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a7c1e6ee4b00f54eb2292a1","contributors":{"authors":[{"text":"Biederman, Joel A.","contributorId":201939,"corporation":false,"usgs":false,"family":"Biederman","given":"Joel","email":"","middleInitial":"A.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":727236,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scott, Russell L.","contributorId":39875,"corporation":false,"usgs":false,"family":"Scott","given":"Russell","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":727237,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arnone, John A.","contributorId":201941,"corporation":false,"usgs":false,"family":"Arnone","given":"John","email":"","middleInitial":"A.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":727238,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jasoni, Richard L.","contributorId":201942,"corporation":false,"usgs":false,"family":"Jasoni","given":"Richard","email":"","middleInitial":"L.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":727239,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Litvak, Marcy E.","contributorId":73932,"corporation":false,"usgs":true,"family":"Litvak","given":"Marcy","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":727240,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moreo, Michael T. 0000-0002-9122-6958 mtmoreo@usgs.gov","orcid":"https://orcid.org/0000-0002-9122-6958","contributorId":2363,"corporation":false,"usgs":true,"family":"Moreo","given":"Michael","email":"mtmoreo@usgs.gov","middleInitial":"T.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":727235,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Papuga, Shirley A.","contributorId":197727,"corporation":false,"usgs":false,"family":"Papuga","given":"Shirley","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":727241,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ponce-Campos, Guillermo E.","contributorId":201945,"corporation":false,"usgs":false,"family":"Ponce-Campos","given":"Guillermo","email":"","middleInitial":"E.","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":727242,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schreiner-McGraw, Adam P.","contributorId":201946,"corporation":false,"usgs":false,"family":"Schreiner-McGraw","given":"Adam","email":"","middleInitial":"P.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":727243,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Vivoni, Enrique R.","contributorId":139052,"corporation":false,"usgs":false,"family":"Vivoni","given":"Enrique","email":"","middleInitial":"R.","affiliations":[{"id":12634,"text":"School of Earth and Space Exploration and School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ","active":true,"usgs":false}],"preferred":false,"id":727244,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70194995,"text":"ofr20181014 - 2018 - Relations between total phosphorus and orthophosphorus concentrations and rainfall, surface-water discharge, and groundwater levels in Big Cypress Seminole Indian Reservation, Florida, 2014–16","interactions":[],"lastModifiedDate":"2018-02-07T10:51:43","indexId":"ofr20181014","displayToPublicDate":"2018-02-06T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1014","title":"Relations between total phosphorus and orthophosphorus concentrations and rainfall, surface-water discharge, and groundwater levels in Big Cypress Seminole Indian Reservation, Florida, 2014–16","docAbstract":"The Seminole Tribe of Florida (the Tribe) is partnering with the U.S. Environmental Protection Agency to develop a numeric phosphorus criterion for the 52,000-acre Big Cypress Seminole Indian Reservation (BCSIR), which is located downgradient of the Everglades Agricultural Area, and of other public and private lands, in southeastern Hendry County and northwestern Broward County in southern Florida. The U.S. Geological Survey (USGS), in cooperation with the Tribe, used water-quality data collected between October 2014 and September 2016 by the Tribe and the South Florida Water Management District (SFWMD), along with data from rainfall gages, surface-water stage and discharge gages, and groundwater monitoring wells, to (1) examine the relations between local hydrology and measured total phosphorus (TP) and orthophosphorus (OP) concentrations and (2) identify explanatory variables for TP concentrations. Of particular concern were conditions when TP exceeded 10 parts per billion (ppb) (0.01 milligram per liter [mg/L]) given that the State of Florida and the Miccosukee Tribe of Indians Alligator Alley Reservation (located downstream of the BCSIR) have adopted a 10-ppb maximum TP criterion for surface waters.
From October 2014 to September 2016, the Tribe collected 47–52 samples at each of nine water-quality sites for analysis of TP and OP, except at one site where 28 samples were collected. For all sites sampled, concentrations of TP (as phosphorus [P]) ranged from less than 0.002 mg/L (2 ppb) to a maximum of nearly 0.50 mg/L (500 ppb), whereas concentrations of OP (as P), the reactive form of inorganic phosphorus readily absorbed by plants and (or) abiotically absorbed, ranged from less than 0.003 mg/L (3 ppb) to a maximum of 0.24 mg/L (240 ppb). The median and interquartile ranges of concentrations of TP and OP in the samples collected in 2014–16 by the Tribe were similar to the median and interquartile ranges of concentrations in samples collected by the SFWMD at nearby sites during the same period. Differences in concentrations can likely be explained by differences in sample collection methods, sampling locations, sample collection time, and the hydrology during sampling or by the number of samples collected. A major limitation of this study was the short duration of sample collection, which covers a limited range of hydrologic conditions within the BCSIR.
The effect of surface-water and groundwater hydrologic conditions on TP and OP concentrations was assessed by using rainfall data and surface-water stage and discharge records. The highest TP and OP concentrations occurred during peak surface-water flows in the canals following long dry periods. Concentrations of TP and OP increased internal to the BCSIR in the western half of the BCSIR during wet periods, but increased concentrations tended to lag behind rainfall events, likely because control structures upstream of sampling sites do not release flows until the water levels in the canals reach predetermined levels. This pattern may indicate that bed sediments in the canals contain high concentrations of phosphorus that becomes resuspended during high flows or that phosphorus salts that had accumulated on dry land during dry periods are carried into the canals by runoff. The largest TP spikes usually occurred at the beginning of high-flow events, but then quickly tapered off even when flows remained high.
Groundwater flows were assessed in the BCSIR by using groundwater level observations from two preexisting USGS monitoring well clusters, each characterized by a shallow well installed in the surficial aquifer system and a deeper well installed in the intermediate aquifer system. Groundwater levels were evaluated with respect to surface-water levels and discharge in the BCSIR during the period of surface-water sampling. During dry conditions water levels in canals were often higher than groundwater levels in the surficial aquifer, indicating the potential for surface water to recharge the surficial aquifer. During wetter conditions, this trend reversed, and there was potential for shallow groundwater discharge into the canals.
From October 2014 to September 2016, concentrations of TP tended to decrease as surface-water inflows moved across the BCSIR from north to south. In both the western and eastern halves of the reservation, the mean concentration of TP was lower in the surface-water outflows from the BCSIR than in the inflows. The mean concentration of TP in the inflows to the western reservation was 0.04 mg/L (40 ppb), whereas the mean concentration of TP in the outflows was 0.03 mg/L (30 ppb). In the eastern reservation, the mean concentration of TP in the inflows was 0.07 mg/L (70 ppb), whereas the mean concentration of TP in the outflows was 0.04 mg/L (40 ppb).
TP and OP concentrations were evaluated relative to other water-quality parameters, including turbidity, suspended solids, nitrate plus nitrite, dissolved oxygen, pH, and specific conductance, to determine if any relations existed between TP and other variables. Weak relations were indicated for turbidity and suspended solids at two sites, which indicates that there may be a relation of increased TP to mobilization of sediment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181014","collaboration":"Prepared in cooperation with the Seminole Tribe of Florida","usgsCitation":"McBride, W.S., and Sifuentes, D.F., 2018, Relations between total phosphorus and orthophosphorus concentrations and rainfall, surface-water discharge, and groundwater levels in Big Cypress Seminole Indian Reservation, Florida, 2014–16: U.S. Geological Survey Open File Report 2018–1014, 63 p., https://doi.org/10.3133/ofr20181014.","productDescription":"xi, 63 p.","numberOfPages":"79","onlineOnly":"Y","ipdsId":"IP-086087","costCenters":[{"id":270,"text":"FLWSC-Tampa","active":true,"usgs":true}],"links":[{"id":351046,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1014/ofr20181014.pdf","text":"Report","size":"6.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018–1014"},{"id":351045,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1014/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Big Cypress Seminole Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.8333,\n 26.1667\n ],\n [\n -81.0833,\n 26.1667\n ],\n [\n -81.0833,\n 26.4167\n ],\n [\n -80.8333,\n 26.4167\n ],\n [\n -80.8333,\n 26.1667\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane
Lutz, FL 33559
Understanding the causes of human-induced earthquakes is paramount to reducing societal risk. We investigated five cases of seismicity associated with hydraulic fracturing (HF) in Ohio since 2013 that, because of their isolation from other injection activities, provide an ideal setting for studying the relations between high-pressure injection and earthquakes. Our analysis revealed two distinct groups: (i) deeper earthquakes in the Precambrian basement, with larger magnitudes (M > 2), b-values < 1, and many post–shut-in earthquakes, versus (ii) shallower earthquakes in Paleozoic rocks ∼400 m below HF, with smaller magnitudes (M < 1), b-values > 1.5, and few post–shut-in earthquakes. Based on geologic history, laboratory experiments, and fault modeling, we interpret the deep seismicity as slip on more mature faults in older crystalline rocks and the shallow seismicity as slip on immature faults in younger sedimentary rocks. This suggests that HF inducing deeper seismicity may pose higher seismic hazards. Wells inducing deeper seismicity produced more water than wells with shallow seismicity, indicating more extensive hydrologic connections outside the target formation, consistent with pore pressure diffusion influencing seismicity. However, for both groups, the 2 to 3 h between onset of HF and seismicity is too short for typical fluid pressure diffusion rates across distances of ∼1 km and argues for poroelastic stress transfer also having a primary influence on seismicity.
There is a critical and growing need for information about subsurface geological properties and processes over sufficiently large areas that can inform key scientific and societal studies. Airborne geophysical methods fill a unique role in Earth observation because of their ability to detect deep subsurface properties at regional scales and with high spatial resolution that cannot be achieved with groundbased measurements. Airborne electromagnetics, or AEM, is one technique that is rapidly emerging as a foundational tool for geological mapping, with widespread application to studies of water and mineral resources, geologic hazards, infrastructure, the cryosphere, and the environment. Applications of AEM are growing worldwide, with rapid developments in instrumentation and data analysis software. In this study, we summarize several recent hydrogeophysical applications of AEM, including examples drawn from a recent survey in the Mississippi Alluvial Plain (MAP). In addition, we discuss developments in computational methods for geophysical and geological model structural uncertainty quantification using AEM data, and how these results are used in a sequential hydrogeophysical approach to characterize hydrologic parameters and prediction uncertainty.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"SEG technical program expanded abstracts 2018","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/segam2018-2989187.1","usgsCitation":"Minsley, B.J., Foks, N.L., Kress, W., and Rigby, J., 2018, Hydrogeophysics at societally relevant scales: Airborne electromagnetic applications and model structural uncertainty quantification, in SEG technical program expanded abstracts 2018, p. 4894-4898, https://doi.org/10.1190/segam2018-2989187.1.","productDescription":"5 p.","startPage":"4894","endPage":"4898","ipdsId":"IP-096781","costCenters":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":382890,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2018-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":809252,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foks, Nathan Leon 0000-0002-4907-3679","orcid":"https://orcid.org/0000-0002-4907-3679","contributorId":203470,"corporation":false,"usgs":true,"family":"Foks","given":"Nathan","email":"","middleInitial":"Leon","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":809253,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kress, Wade 0000-0002-6833-028X","orcid":"https://orcid.org/0000-0002-6833-028X","contributorId":203539,"corporation":false,"usgs":true,"family":"Kress","given":"Wade","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":809254,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rigby, James R. 0000-0002-5611-6307","orcid":"https://orcid.org/0000-0002-5611-6307","contributorId":196374,"corporation":false,"usgs":false,"family":"Rigby","given":"James R.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":809255,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70269688,"text":"70269688 - 2018 - Satellite psychrometric formulation of the operational simplified surface energy balance (SSEBop) model for quantifying and mapping evapotranspiration","interactions":[],"lastModifiedDate":"2025-07-30T14:57:03.354604","indexId":"70269688","displayToPublicDate":"2018-02-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":833,"text":"Applied Engineering in Agriculture","active":true,"publicationSubtype":{"id":10}},"title":"Satellite psychrometric formulation of the operational simplified surface energy balance (SSEBop) model for quantifying and mapping evapotranspiration","docAbstract":"Remote sensing-based evapotranspiration (ET) can be derived using various methods, from soil moisture accounting to vegetation-index based approaches to simple and complex surface energy balance techniques. Due to the complexity of fully representing and parameterizing ET sub-processes, different models tend to diverge in their estimations. However, most models appear to provide reasonable estimations that can meet user requirements for seasonal water use estimation and drought monitoring. One such model is the Operational Simplified Surface Energy Balance (SSEBop). This study presents a formulation of the SSEBop model using the psychrometric principle for vapor pressure/relative humidity measurements where the “dry-bulb” and “wet-bulb” equivalent readings can be obtained from satellite-based land surface temperature estimates. The difference in temperature between the dry (desired location) and wet limit (reference value) is directly correlated to the soil-vegetation composite moisture status (surface humidity) and thus producing a fractional value (0-1) to scale the reference ET. The reference ET is independently calculated using available weather data through the standardized Penman-Monteith equation. Satellite Psychrometric Approach (SPA) explains the SSEBop model more effectively than the energy balance principle because SSEBop does not solve all terms of the surface energy balance such as sensible and ground-heat fluxes. The SPA explanation demonstrates the psychrometric constant for the air can be readily adapted to a comparable constant for the surface, thus allowing the creation of a “surface” psychrometric constant that is unique to a location and day-of-year. This new surface psychrometric constant simplifies the calculation and explanation of satellite-based ET for several applications in agriculture and hydrology. The SPA formulation of SSEBop was found to be an enhancement of the ET equation formulated in 1977 by pioneering researchers. With only two key parameters, improved model results can be obtained using a one-time calibration for any bias correction. The model can be set up quickly for routine monitoring and assessment of ET at landscape scales and beyond.","language":"English","publisher":"American Society of Agricultural and Biological Engineers","doi":"10.13031/aea.12614","usgsCitation":"Senay, G.B., 2018, Satellite psychrometric formulation of the operational simplified surface energy balance (SSEBop) model for quantifying and mapping evapotranspiration: Applied Engineering in Agriculture, v. 34, no. 3, p. 555-566, https://doi.org/10.13031/aea.12614.","productDescription":"12 p.","startPage":"555","endPage":"566","ipdsId":"IP-094223","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":493303,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.13031/aea.12614","text":"Publisher Index Page"},{"id":493186,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109.97952160594414,\n 32.43068897810058\n ],\n [\n -109.97952160594414,\n 32.11045287871744\n ],\n [\n -109.67205252300127,\n 32.11045287871744\n ],\n [\n -109.67205252300127,\n 32.43068897810058\n ],\n [\n -109.97952160594414,\n 32.43068897810058\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"34","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Senay, Gabriel B. 0000-0002-8810-8539 senay@usgs.gov","orcid":"https://orcid.org/0000-0002-8810-8539","contributorId":3114,"corporation":false,"usgs":true,"family":"Senay","given":"Gabriel","email":"senay@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":944449,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ]}