The Full Equations Model (FEQ) is a computer program that solves the full, dynamic equations of motion for one-dimensional unsteady hydraulic flow in open channels and through control structures. As a result, hydrologists have used FEQ to design and operate flood-control structures, delineate inundation maps, and analyze peak-flow impacts. To aid in fighting floods, hydrologists are using the software to develop a system that uses flood-plain models to simulate real-time streamflow.
Input files for FEQ are composed of text files that contain large amounts of parameters, data, and instructions that are written in a format exclusive to FEQ. Although documentation exists that can aid in the creation and editing of these input files, new users face a steep learning curve in order to understand the specific format and language of the files.
FEQinput provides a set of tools to help a new user overcome the steep learning curve associated with creating and modifying input files for the FEQ hydraulic model and the related utility tool, Full Equations Utilities (FEQUTL).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173072","usgsCitation":"Ancalle, D.S., Ancalle, P.J., and Domanski, M.M., 2017, FEQinput—An editor for the full equations (FEQ) hydraulic modeling system: U.S. Geological Survey Fact Sheet 2017–3072, 4 p., https://doi.org/10.3133/fs20173072.","productDescription":"Report: 4 p.; Project Site","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-082519","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":347141,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3072/fs20173072.pdf","text":"Report","size":"770 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3072"},{"id":347345,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://il.water.usgs.gov/proj/feq/software/feqinput/","text":"Software"},{"id":347140,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3072/coverthb2.jpg"}],"contact":"Director, Illinois-Iowa Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801
Changing magnitude, frequency, and timing of precipitation can influence aquatic-system hydrological, geochemical, and biological processes, in some cases resulting in system-wide shifts to an alternate state. Since the early 1990s, the southern Prairie Pothole Region has been subjected to an extended period of increased wetness resulting in marked changes to aquatic systems defining this region. We explored numerous lines of evidence to identify: (1) how the recent wet period compared to historical variability, (2) hydrological, geochemical, and biological responses, and (3) how these responses might represent a state shift in the region’s wetland ecosystems. We analyzed long-term climate records and compared how different hydrological variables responded in this wet period compared to decades before the observed shift. Additionally, we used multi-decadal records of waterfowl population and subsurface tile drain records to explore wildlife and human responses to a shifting climate. Since 1993, a novel precipitation regime corresponded with increased pond numbers, ponded-water depths, lake levels, stream flows, groundwater heights, soil-moisture, waterfowl populations, and installation of subsurface tile drains in agricultural fields. These observed changes reflect an alteration in water storage and movement across the landscape that in turn has altered solute sources and concentrations of prairie-pothole wetlands and has increased pond permanence. Combined, these changes represent significant evidence for a state shift in the ecohydrological functioning of the region’s wetland ecosystems, a shift that may require a significant refinement of the previously developed “wetland continuum” concept.
","language":"English","publisher":"Springer","doi":"10.1007/s10584-017-2097-7","usgsCitation":"McKenna, O.P., Mushet, D.M., Rosenberry, D.O., and LaBaugh, J.W., 2017, Evidence for a climate-induced ecohydrological state shift in wetland ecosystems of the southern Prairie Pothole Region: Climatic Change, v. 145, no. 3-4, p. 273-287, https://doi.org/10.1007/s10584-017-2097-7.","productDescription":"15 p.","startPage":"273","endPage":"287","ipdsId":"IP-085972","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469385,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://link.springer.com/10.1007/s10584-017-2097-7","text":"External Repository"},{"id":347715,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Prairie Pothole Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.35986328125,\n 45.93969078234\n ],\n [\n -96.5643310546875,\n 45.93969078234\n ],\n [\n -96.5643310546875,\n 48.99824008113872\n ],\n [\n -101.35986328125,\n 48.99824008113872\n ],\n [\n -101.35986328125,\n 45.93969078234\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"145","issue":"3-4","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-26","publicationStatus":"PW","scienceBaseUri":"59f83a2fe4b063d5d309809d","contributors":{"authors":[{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":716514,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":716515,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":716516,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"LaBaugh, James W. 0000-0002-4112-2536 jlabaugh@usgs.gov","orcid":"https://orcid.org/0000-0002-4112-2536","contributorId":1311,"corporation":false,"usgs":true,"family":"LaBaugh","given":"James","email":"jlabaugh@usgs.gov","middleInitial":"W.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":716517,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70192397,"text":"70192397 - 2017 - Climate change and alpine stream biology: progress, challenges, and opportunities for the future","interactions":[],"lastModifiedDate":"2017-10-26T09:22:57","indexId":"70192397","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1023,"text":"Biological Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Climate change and alpine stream biology: progress, challenges, and opportunities for the future","docAbstract":"In alpine regions worldwide, climate change is dramatically altering ecosystems and affecting biodiversity in many ways. For streams, receding alpine glaciers and snowfields, paired with altered precipitation regimes, are driving shifts in hydrology, species distributions, basal resources, and threatening the very existence of some habitats and biota. Alpine streams harbour substantial species and genetic diversity due to significant habitat insularity and environmental heterogeneity. Climate change is expected to affect alpine stream biodiversity across many levels of biological resolution from micro- to macroscopic organisms and genes to communities. Herein, we describe the current state of alpine stream biology from an organism-focused perspective. We begin by reviewing seven standard and emerging approaches that combine to form the current state of the discipline. We follow with a call for increased synthesis across existing approaches to improve understanding of how these imperiled ecosystems are responding to rapid environmental change. We then take a forward-looking viewpoint on how alpine stream biologists can make better use of existing data sets through temporal comparisons, integrate remote sensing and geographic information system (GIS) technologies, and apply genomic tools to refine knowledge of underlying evolutionary processes. We conclude with comments about the future of biodiversity conservation in alpine streams to confront the daunting challenge of mitigating the effects of rapid environmental change in these sentinel ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1111/brv.12319","usgsCitation":"Hotaling, S., Finn, D.S., Giersch, J., Weisrock, D.W., and Jacobsen, D., 2017, Climate change and alpine stream biology: progress, challenges, and opportunities for the future: Biological Reviews, v. 92, no. 4, p. 2024-2045, https://doi.org/10.1111/brv.12319.","productDescription":"22 p.","startPage":"2024","endPage":"2045","ipdsId":"IP-079039","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":469397,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/brv.12319","text":"External Repository"},{"id":347387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"92","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-20","publicationStatus":"PW","scienceBaseUri":"59f1a29be4b0220bbd9d9ed4","contributors":{"authors":[{"text":"Hotaling, Scott 0000-0002-5965-0986","orcid":"https://orcid.org/0000-0002-5965-0986","contributorId":176860,"corporation":false,"usgs":false,"family":"Hotaling","given":"Scott","email":"","affiliations":[],"preferred":false,"id":715676,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finn, Debra S.","contributorId":198312,"corporation":false,"usgs":false,"family":"Finn","given":"Debra","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":715677,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Giersch, J. Joseph 0000-0001-7818-3941 jgiersch@usgs.gov","orcid":"https://orcid.org/0000-0001-7818-3941","contributorId":4022,"corporation":false,"usgs":true,"family":"Giersch","given":"J. Joseph","email":"jgiersch@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":false,"id":715675,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weisrock, David W.","contributorId":198313,"corporation":false,"usgs":false,"family":"Weisrock","given":"David","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":715678,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jacobsen, Dean 0000-0001-5137-297X","orcid":"https://orcid.org/0000-0001-5137-297X","contributorId":198314,"corporation":false,"usgs":false,"family":"Jacobsen","given":"Dean","email":"","affiliations":[],"preferred":false,"id":715679,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70192341,"text":"70192341 - 2017 - Groundwater-level trends in the U.S. glacial aquifer system, 1964-2013","interactions":[],"lastModifiedDate":"2017-11-06T15:23:39","indexId":"70192341","displayToPublicDate":"2017-10-25T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater-level trends in the U.S. glacial aquifer system, 1964-2013","docAbstract":"The glacial aquifer system in the United States is a major source of water supply but previous work on historical groundwater trends across the system is lacking. Trends in annual minimum, mean, and maximum groundwater levels for 205 monitoring wells were analyzed across three regions of the system (East, Central, West Central) for four time periods: 1964-2013, 1974-2013, 1984-2013, and 1994-2013. Trends were computed separately for wells in the glacial aquifer system with low potential for human influence on groundwater levels and ones with high potential influence from activities such as groundwater pumping. Generally there were more wells with significantly increasing groundwater levels (levels closer to ground surface) than wells with significantly decreasing levels. The highest numbers of significant increases for all four time periods were with annual minimum and/or mean levels. There were many more wells with significant increases from 1964 to 2013 than from more recent periods, consistent with low precipitation in the 1960s. Overall there were low numbers of wells with significantly decreasing trends regardless of time period considered; the highest number of these were generally for annual minimum groundwater levels at wells with likely human influence. There were substantial differences in the number of wells with significant groundwater-level trends over time, depending on whether the historical time series are assumed to be independent, have short-term persistence, or have long-term persistence. Mean annual groundwater levels have significant lag-one-year autocorrelation at 26.0% of wells in the East region, 65.4% of wells in the Central region, and 100% of wells in the West Central region. Annual precipitation across the glacial aquifer system, on the other hand, has significant autocorrelation at only 5.5% of stations, about the percentage expected due to chance.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.07.055","usgsCitation":"Hodgkins, G.A., Dudley, R.W., Nielsen, M.G., Renard, B., and Qi, S.L., 2017, Groundwater-level trends in the U.S. glacial aquifer system, 1964-2013: Journal of Hydrology, v. 553, p. 289-303, https://doi.org/10.1016/j.jhydrol.2017.07.055.","productDescription":"15 p.","startPage":"289","endPage":"303","ipdsId":"IP-081195","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":461379,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2017.07.055","text":"Publisher Index Page"},{"id":347310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5091","title":"Simulation of daily streamflow for 12 river basins in western Iowa using the Precipitation-Runoff Modeling System","docAbstract":"The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, constructed Precipitation-Runoff Modeling System models to estimate daily streamflow for 12 river basins in western Iowa that drain into the Missouri River. The Precipitation-Runoff Modeling System is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of streamflow and general drainage basin hydrology to various combinations of climate and land use. Calibration periods for each basin varied depending on the period of record available for daily mean streamflow measurements at U.S. Geological Survey streamflow-gaging stations.
A geographic information system tool was used to delineate each basin and estimate initial values for model parameters based on basin physical and geographical features. A U.S. Geological Survey automatic calibration tool that uses a shuffled complex evolution algorithm was used for initial calibration, and then manual modifications were made to parameter values to complete the calibration of each basin model. The main objective of the calibration was to match daily discharge values of simulated streamflow to measured daily discharge values. The Precipitation-Runoff Modeling System model was calibrated at 42 sites located in the 12 river basins in western Iowa.
The accuracy of the simulated daily streamflow values at the 42 calibration sites varied by river and by site. The models were satisfactory at 36 of the sites based on statistical results. Unsatisfactory performance at the six other sites can be attributed to several factors: (1) low flow, no flow, and flashy flow conditions in headwater subbasins having a small drainage area; (2) poor representation of the groundwater and storage components of flow within a basin; (3) lack of accounting for basin withdrawals and water use; and (4) limited availability and accuracy of meteorological input data. The Precipitation-Runoff Modeling System models of 12 river basins in western Iowa will provide water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites and aid in environmental studies, hydraulic design, water management, and water-quality projects.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175091","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Christiansen, D.E., Haj, A.E., and Risely, J.C., 2017, Simulation of daily streamflow for 12 river basins in western Iowa using the Precipitation-Runoff Modeling System: U.S. Geological Survey Scientific Investigations Report 2017–5091, 27 p., https://doi.org/10.3133/sir20175091. 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Iowa Water Science Center
U.S. Geological Survey
P.O. Box 1230
Iowa City, IA 52240
The Chesapeake Bay (CB) basin is under a total maximum daily load (TMDL) mandate to reduce nitrogen, phosphorus, and sediment loads to the bay. Identifying shifts in the hydro-climatic regime may help explain observed trends in water quality. To identify potential shifts, hydrologic data (1927–2014) for 27 watersheds in the CB basin were analyzed to determine the relationships among long-term precipitation and stream discharge trends. The amount, frequency, and intensity of precipitation increased from 1910 to 1996 in the eastern U.S., with the observed increases greater in the northeastern U.S. than the southeastern U.S. The CB watershed spans the north-to-south gradient in precipitation increases, and hydrologic differences have been observed in watersheds north relative to watersheds south of the Pennsylvania—Maryland (PA-MD) border. Time series of monthly mean precipitation data specific to each of 27 watersheds were derived from the Precipitation-elevation Regression on Independent Slopes Model (PRISM) dataset, and monthly mean stream-discharge data were obtained from U.S. Geological Survey streamgage records. All annual precipitation trend slopes in the 18 watersheds north of the PA-MD border were greater than or equal to those of the nine south of that border. The magnitude of the trend slopes for 1927–2014 in both precipitation and discharge decreased in a north-to-south pattern. Distributions of the monthly precipitation and discharge datasets were assembled into percentiles for each year for each watershed. Multivariate correlation of precipitation and discharge within percentiles among the groups of northern and southern watersheds indicated only weak associations. Regional-scale average behaviors of trends in the distribution of precipitation and discharge annual percentiles differed between the northern and southern watersheds. In general, the linkage between precipitation and discharge was weak, with the linkage weaker in the northern watersheds compared to those in the south. On the basis of simple linear regression, 26 of the 27 watersheds are projected to have higher annual mean discharge in 2025, the target date for implementation of the TMDL for the CB basin.
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0000-0001-6330-478X dlmoyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6330-478X","contributorId":174389,"corporation":false,"usgs":true,"family":"Moyer","given":"Douglas","email":"dlmoyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":713215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mills, Aaron L.","contributorId":189745,"corporation":false,"usgs":false,"family":"Mills","given":"Aaron L.","affiliations":[],"preferred":false,"id":713216,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192233,"text":"70192233 - 2017 - Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information","interactions":[],"lastModifiedDate":"2017-10-24T12:21:45","indexId":"70192233","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information","docAbstract":"Although river discharge is a fundamental hydrologic quantity, conventional methods of streamgaging are impractical, expensive, and potentially dangerous in remote locations. This study evaluated the potential for measuring discharge via various forms of remote sensing, primarily thermal imaging of flow velocities but also spectrally-based depth retrieval from passive optical image data. We acquired thermal image time series from bridges spanning five streams in Alaska and observed strong agreement between velocities measured in situ and those inferred by Particle Image Velocimetry (PIV), which quantified advection of thermal features by the flow. The resulting surface velocities were converted to depth-averaged velocities by applying site-specific, calibrated velocity indices. Field spectra from three clear-flowing streams provided strong relationships between depth and reflectance, suggesting that, under favorable conditions, spectrally-based bathymetric mapping could complement thermal PIV in a hybrid approach to remote sensing of river discharge; this strategy would not be applicable to larger, more turbid rivers, however. A more flexible and efficient alternative might involve inferring depth from thermal data based on relationships between depth and integral length scales of turbulent fluctuations in temperature, captured as variations in image brightness. We observed moderately strong correlations for a site-aggregated data set that reduced station-to-station variability but encompassed a broad range of depths. Discharges calculated using thermal PIV-derived velocities were within 15% of in situ measurements when combined with depths measured directly in the field or estimated from field spectra and within 40% when the depth information also was derived from thermal images. The results of this initial, proof-of-concept investigation suggest that remote sensing techniques could facilitate measurement of river discharge.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.09.004","usgsCitation":"Legleiter, C.J., Kinzel, P.J., and Nelson, J.M., 2017, Remote measurement of river discharge using thermal particle image velocimetry (PIV) and various sources of bathymetric information: Journal of Hydrology, v. 554, p. 490-506, https://doi.org/10.1016/j.jhydrol.2017.09.004.","productDescription":"17 p.","startPage":"490","endPage":"506","ipdsId":"IP-084918","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":469406,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2017.09.004","text":"Publisher Index Page"},{"id":438181,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ST7N0J","text":"USGS data release","linkHelpText":"Thermal image time series from rivers in Alaska, September 18-20, 2016"},{"id":438180,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7J964K7","text":"USGS data release","linkHelpText":"ADCP data from rivers in Alaska, September 18-20, 2016"},{"id":438179,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7M906TJ","text":"USGS data release","linkHelpText":"Field spectra from rivers in Alaska, September 19-21, 2016"},{"id":347224,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"554","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59f0511ee4b0220bbd9a1d64","contributors":{"authors":[{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@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":714904,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kinzel, Paul J. 0000-0002-6076-9730 pjkinzel@usgs.gov","orcid":"https://orcid.org/0000-0002-6076-9730","contributorId":743,"corporation":false,"usgs":true,"family":"Kinzel","given":"Paul","email":"pjkinzel@usgs.gov","middleInitial":"J.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"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},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":714905,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nelson, Jonathan M. 0000-0002-7632-8526 jmn@usgs.gov","orcid":"https://orcid.org/0000-0002-7632-8526","contributorId":2812,"corporation":false,"usgs":true,"family":"Nelson","given":"Jonathan","email":"jmn@usgs.gov","middleInitial":"M.","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":714906,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70192243,"text":"70192243 - 2017 - Quantifying the effects of wildfire on changes in soil properties by surface burning of soils from the Boulder Creek Critical Zone Observatory","interactions":[],"lastModifiedDate":"2017-10-24T12:14:23","indexId":"70192243","displayToPublicDate":"2017-10-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying the effects of wildfire on changes in soil properties by surface burning of soils from the Boulder Creek Critical Zone Observatory","docAbstract":"Study region
This study used intact soil cores collected at the Boulder Creek Critical Zone Observatory near Boulder, Colorado, USA to explore fire impacts on soil properties.
Study focus
Three soil scenarios were considered: unburned control soils, and low- and high-temperature burned soils. We explored simulated fire impacts on field-saturated hydraulic conductivity, dry bulk density, total organic carbon, and infiltration processes during rainfall simulations.
New hydrological insights for the region
Soils burned to high temperatures became more homogeneous with depth with respect to total organic carbon and bulk density, suggesting reductions in near-surface porosity. Organic matter decreased significantly with increasing soil temperature. Tension infiltration experiments suggested a decrease in infiltration rates from unburned to low-temperature burned soils, and an increase in infiltration rates in high-temperature burned soils. Non-parametric statistical tests showed that field-saturated hydraulic conductivity similarly decreased from unburned to low-temperature burned soils, and then increased with high-temperature burned soils. We interpret these changes result from the combustion of surface and near-surface organic materials, enabling water to infiltrate directly into soil instead of being stored in the litter and duff layer at the surface. Together, these results indicate that fire-induced changes in soil properties from low temperatures were not as drastic as high temperatures, but that reductions in surface soil water repellency in high temperatures may increase infiltration relative to low temperatures.
To understand the moisture regime at the southern slopes of Mt. Kilimanjaro, we analysed the isotopic variability of oxygen (δ18O) and hydrogen (δD) of rainfall, throughfall, and fog from a total of 2,140 samples collected weekly over 2 years at 9 study sites along an elevation transect ranging from 950 to 3,880 m above sea level. Precipitation in the Kilimanjaro tropical rainforests consists of a combination of rainfall, throughfall, and fog. We defined local meteoric water lines for all 3 precipitation types individually and the overall precipitation, δDprec = 7.45 (±0.05) × δ18Oprec + 13.61 (±0.20), n = 2,140, R2 = .91, p < .001. We investigated the precipitation-type-specific stable isotope composition and analysed the effects of amount, altitude, and temperature. Aggregated annual mean values revealed isotope composition of rainfall as most depleted and fog water as most enriched in heavy isotopes at the highest elevation research site. We found an altitude effect of δ18Orain = −0.11‰ × 100 m−1, which varied according to precipitation type and season. The relatively weak isotope or altitude gradient may reveal 2 different moisture sources in the research area: (a) local moisture recycling and (b) regional moisture sources. Generally, the seasonality of δ18Orain values follows the bimodal rainfall distribution under the influences of south- and north-easterly trade winds. These seasonal patterns of isotopic composition were linked to different regional moisture sources by analysing Hybrid Single Particle Lagrangian Integrated Trajectory backward trajectories. Seasonality of dexcess values revealed evidence of enhanced moisture recycling after the onset of the rainy seasons. This comprehensive dataset is essential for further research using stable isotopes as a hydrological tracer of sources of precipitation that contribute to water resources of the Kilimanjaro region.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11311","usgsCitation":"Otte, I., Detsch, F., Gutlein, A., Scholl, M.A., Kiese, R., Appelhans, T., and Nauss, T., 2017, Seasonality of stable isotope composition of atmospheric water input at the southern slopes of Mt. Kilimanjaro, Tanzania: Hydrological Processes, v. 31, no. 22, p. 3932-3947, https://doi.org/10.1002/hyp.11311.","productDescription":"16 p.","startPage":"3932","endPage":"3947","ipdsId":"IP-089904","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":469417,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.11311","text":"Publisher Index Page"},{"id":347122,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tanzania","otherGeospatial":"Mt. Kilimanjaro","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 37.14202880859375,\n -3.2412964891479614\n ],\n [\n 37.57530212402344,\n -3.2412964891479614\n ],\n [\n 37.57530212402344,\n -2.956069891317356\n ],\n [\n 37.14202880859375,\n -2.956069891317356\n ],\n [\n 37.14202880859375,\n -3.2412964891479614\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"22","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-15","publicationStatus":"PW","scienceBaseUri":"59eeffa0e4b0220bbd988f4d","contributors":{"authors":[{"text":"Otte, Insa","contributorId":198023,"corporation":false,"usgs":false,"family":"Otte","given":"Insa","email":"","affiliations":[],"preferred":false,"id":714826,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Detsch, Florian","contributorId":198024,"corporation":false,"usgs":false,"family":"Detsch","given":"Florian","email":"","affiliations":[],"preferred":false,"id":714827,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gutlein, Adrian","contributorId":198025,"corporation":false,"usgs":false,"family":"Gutlein","given":"Adrian","email":"","affiliations":[],"preferred":false,"id":714828,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scholl, Martha A. 0000-0001-6994-4614 mascholl@usgs.gov","orcid":"https://orcid.org/0000-0001-6994-4614","contributorId":1920,"corporation":false,"usgs":true,"family":"Scholl","given":"Martha","email":"mascholl@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":714825,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kiese, Ralf","contributorId":198026,"corporation":false,"usgs":false,"family":"Kiese","given":"Ralf","email":"","affiliations":[],"preferred":false,"id":714829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Appelhans, Tim","contributorId":198027,"corporation":false,"usgs":false,"family":"Appelhans","given":"Tim","email":"","affiliations":[],"preferred":false,"id":714830,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nauss, Thomas","contributorId":198028,"corporation":false,"usgs":false,"family":"Nauss","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":714831,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70191807,"text":"sir20175097 - 2017 - Simulation of groundwater and surface-water flow in the upper Deschutes Basin, Oregon","interactions":[],"lastModifiedDate":"2017-10-23T11:30:00","indexId":"sir20175097","displayToPublicDate":"2017-10-20T00:00:00","publicationYear":"2017","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":"2017-5097","title":"Simulation of groundwater and surface-water flow in the upper Deschutes Basin, Oregon","docAbstract":"This report describes a hydrologic model for the upper Deschutes Basin in central Oregon developed using the U.S. Geological Survey (USGS) integrated Groundwater and Surface-Water Flow model (GSFLOW). The upper Deschutes Basin, which drains much of the eastern side of the Cascade Range in Oregon, is underlain by large areas of permeable volcanic rock. That permeability, in combination with the large annual precipitation at high elevations, results in a substantial regional aquifer system and a stream system that is heavily groundwater dominated.
The upper Deschutes Basin is also an area of expanding population and increasing water demand for public supply and agriculture. Surface water was largely developed for agricultural use by the mid-20th century, and is closed to additional appropriations. Consequently, water users look to groundwater to satisfy the growing demand. The well‑documented connection between groundwater and the stream system, and the institutional and legal restrictions on streamflow depletion by wells, resulted in the Oregon Water Resources Department (OWRD) instituting a process whereby additional groundwater pumping can be permitted only if the effects to streams are mitigated, for example, by reducing permitted surface-water diversions. Implementing such a program requires understanding of the spatial and temporal distribution of effects to streams from groundwater pumping. A groundwater model developed in the early 2000s by the USGS and OWRD has been used to provide insights into the distribution of streamflow depletion by wells, but lacks spatial resolution in sensitive headwaters and spring areas.
The integrated model developed for this project, based largely on the earlier model, has a much finer grid spacing allowing resolution of sensitive headwater streams and important spring areas, and simulates a more complete set of surface processes as well as runoff and groundwater flow. In addition, the integrated model includes improved representation of subsurface geology and explicitly simulates the effects of hydrologically important fault zones not included in the previous model.
The upper Deschutes Basin GSFLOW model was calibrated using an iterative trial and error approach using measured water-level elevations (water levels) from 800 wells, 144 of which have time series of 10 or more measurements. Streamflow was calibrated using data from 21 gage locations. At 14 locations where measured flows are heavily influenced by reservoir operations and irrigation diversions, so called “naturalized” flows, with the effects of reservoirs and diversion removed, developed by the Bureau of Reclamation, were used for calibration. Surface energy and moisture processes such as solar radiation, snow accumulation and melting, and evapotranspiration were calibrated using national datasets as well as data from long-term measurement sites in the basin. The calibrated Deschutes GSFLOW model requires daily precipitation, minimum and maximum air temperature data, and monthly data describing groundwater pumping and artificial recharge from leaking irrigation canals (which are a significant source of groundwater recharge).
The calibrated model simulates the geographic distribution of hydraulic head over the 5,000 ft range measured in the basin, with a median absolute residual of about 53 ft. Temporal variations in head resulting from climate cycles, pumping, and canal leakage are well simulated over the model area. Simulated daily streamflow matches gaged flows or calculated naturalized flows for streams including the Crooked and Metolius Rivers, and lower parts of the mainstem Deschutes River. Seasonal patterns of runoff are less well fit in some upper basin streams. Annual water balances of streamflow are good over most of the model domain. Model fit and overall capabilities are appropriate for the objectives of the project.
The integrated model results confirm findings from other studies and models indicating that most streamflow in the upper Deschutes Basin comes directly from groundwater discharge. The integrated model provides additional insights about the components of streamflow including direct groundwater discharge to streams, interflow, groundwater discharge to the land surface (Dunnian flow), and direct runoff (Hortonian flow). The new model provides improved capability for exploring the timing and distribution of
streamflow capture by wells, and the hydrologic response to changes in other external stresses such as canal operation, irrigation, and drought. Because the model uses basic meteorological data as the primary input; and simulates surface energy and moisture balances, groundwater recharge and flow, and all components of streamflow; it is well suited for exploring the hydrologic response to climate change, although no such simulations are included in this report.
The model was developed as a tool for future application; however, example simulations are provided in this report. In the example simulations, the model is used to explore the influence of well location and geologic structure on stream capture by pumping wells. Wells were simulated at three locations within a 12-mi area close to known groundwater discharge areas and crossed by a regional fault zone. Simulations indicate that the magnitude and timing of stream capture from pumping is largely controlled by the geographic location of the wells, but that faults can have a large influence on the propagation of pumping stresses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175097","collaboration":"Prepared in cooperation with the Oregon Water Resources Department","usgsCitation":"Gannett, M.W., Lite, K.E., Jr., Risley, J.C., Pischel, E.M., and La Marche, J.L., 2017, Simulation of groundwater and surface-water flow in the upper Deschutes Basin, Oregon: U.S. Geological Survey Scientific Investigations Report 2017-5097, 68 p., https://doi.org/10.3133/sir20175097.","productDescription":"Report: viii, 68 p.; Model Archive","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-085102","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":347011,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.5066/F7154F9K","text":"Model Archive","description":"SIR 2017-5097 Model Archive"},{"id":346984,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5097/coverthb.jpg"},{"id":346985,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5097/sir20175097.pdf","text":"Report","size":"5.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5097"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Deschutes Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.19268798828126,\n 43.395069512861355\n ],\n [\n -120.7452392578125,\n 43.395069512861355\n ],\n [\n -120.7452392578125,\n 44.939529212272305\n ],\n [\n -122.19268798828126,\n 44.939529212272305\n ],\n [\n -122.19268798828126,\n 43.395069512861355\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
The In-Situ Aqua TROLL 400 (Aqua TROLL 400) was tested at the U.S. Geological Survey (USGS) Hydrologic Instrumentation Facility (HIF) against known standards over the Aqua TROLL 400’s operating temperature to verify the manufacturer’s stated accuracy specifications and the USGS recommendations for pH, dissolved oxygen (DO), and specific conductance (SC). The Aqua TROLL 400 manufacturer’s specifications are within the USGS recommendations for all parameters tested, except for DO, which is outside the USGS recommendation at DO concentrations of 8.0 milligrams per liter (mg/L) and higher. The Aqua TROLL 400 was compliant with Serial Digital Interface at 1200 baud (SDI-12) version 1.3. During laboratory testing of pH, the Aqua TROLL 400 sonde met the U.S. Geological Survey “National Field Manual for the Collection of Water-Quality Data” (NFM) recommendations for pH at all values tested, except at 4 degrees Celsius (°C) at pH 9.395 and pH 3.998. The Aqua TROLL 400 met the manufacturer specifications for pH at all values tested, except for pH buffers 3.998, 9.395, and 10.245 at 4 °C; pH 2.990 and 3.998 at 15 °C; and pH 3.040 at 40 °C. The Aqua TROLL 400 met the NFM recommendations at 93.7 percent of the SC values tested and met the manufacturer’s accuracy specifications at 56.3 percent of the SC values tested. During the laboratory testing for DO, the Aqua TROLL 400 met the manufacturer specifications, except at 5.55 mg/L, and met the NFM recommendations at all concentrations tested. An Aqua TROLL 400 was field tested at USGS Station 02492620, National Space Technology Laboratories (NSTL) Station, Mississippi, on the Pearl River for 6 weeks and showed good agreement with the well-maintained site sonde data for pH, DO, temperature, and SC.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171086","usgsCitation":"Tillman, E.F., 2017, HIF evaluation of In-Situ Aqua TROLL 400: U.S. Geological Survey Open-File Report, 2017–1086, 35 p., https://doi.org/10.3133/ofr20171086.","productDescription":"vi, 35 p.","numberOfPages":"41","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-076079","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":346630,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1086/coverthb.jpg"},{"id":346631,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1086/ofr20171086.pdf","text":"Report","size":"856 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1086"}],"contact":"Chief, Hydrologic Instrumentation Facility
U.S. Geological Survey
Building 2101
Stennis Space Center, MS 39529
The oxygen isotopic composition (δ18O) of land snail shells can be a valuable paleoenvironmental archive if the climatic parameters that influence the isotopic system are fully understood. Previous calibration studies have examined a limited number of species or individuals, and most have focused on larger (> 10 mm) taxa, which do not represent the dominant shell material in the Quaternary fossil record. In this study, we evaluate the δ18O values of small land snails (< 10 mm), which are common in modern settings and are often preserved in a wide array of Quaternary geologic and archeologic deposits. Our primary goal was to determine if coexisting species record equivalent isotopic information in their shells, regardless of differences in their ecology, dietary habits, behavior, and/or body size. We collected and analyzed 265 individuals of 11 species from 12 sites in northwest Minnesota (USA), which exhibits extremely abundant and diverse terrestrial malacofauna in North America. We did not observe significant correlations between shell δ18O values and the type of ecosystem (forest/grassland) or hydrologic setting (upland/lowland). However, the majority of species differed significantly in shell δ18O values. Larger taxa (Catinella, Succinea, Discus) consistently yielded higher δ18O values than smaller taxa (Euconulus, Gastrocopta, Hawaiia, Vallonia), by up to ~ 3‰. These isotopic offsets among sympatric taxa could be attributed to a number of physical, behavioral, and/or evolutionary traits, including the ability of larger species to tolerate drier conditions better than their smaller counterparts, differences in their preferred microhabitats or phylogentic non-independence. Regardless of the reason, our results imply that researchers should not combine isotopic data from different types of land snails without first investigating modern specimens to determine if it is appropriate. Moreover, our data suggest that combining instrumental climate data, a snail flux-balance model, and shell δ18O values can help us to better understand the ecology of land snails.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.palaeo.2017.07.029","usgsCitation":"Yanes, Y., Nekola, J.C., Rech, J.A., and Pigati, J., 2017, Oxygen stable isotopic disparities among sympatric small land snail species from northwest Minnesota, USA: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 485, p. 715-722, https://doi.org/10.1016/j.palaeo.2017.07.029.","productDescription":"8 p.","startPage":"715","endPage":"722","ipdsId":"IP-088424","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":469430,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1776162","text":"Publisher Index Page"},{"id":346883,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.22351074218749,\n 46\n ],\n [\n -95,\n 46\n ],\n [\n -95,\n 48.99824008113872\n ],\n [\n -97.22351074218749,\n 48.99824008113872\n ],\n [\n -97.22351074218749,\n 46\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"485","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e86831e4b05fe04cd4d1cc","contributors":{"authors":[{"text":"Yanes, Yurena","contributorId":197219,"corporation":false,"usgs":false,"family":"Yanes","given":"Yurena","email":"","affiliations":[],"preferred":false,"id":712969,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nekola, Jeffrey C.","contributorId":26214,"corporation":false,"usgs":false,"family":"Nekola","given":"Jeffrey","email":"","middleInitial":"C.","affiliations":[{"id":7000,"text":"Department of Biology, University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":712970,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rech, Jason A.","contributorId":117323,"corporation":false,"usgs":false,"family":"Rech","given":"Jason","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":712971,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pigati, Jeffery S. jpigati@usgs.gov","contributorId":140289,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffery S.","email":"jpigati@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":false,"id":712968,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191549,"text":"70191549 - 2017 - Millennial-scale variability in the local radiocarbon reservoir age of south Florida during the Holocene","interactions":[],"lastModifiedDate":"2017-10-17T10:34:50","indexId":"70191549","displayToPublicDate":"2017-10-17T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3216,"text":"Quaternary Geochronology","active":true,"publicationSubtype":{"id":10}},"title":"Millennial-scale variability in the local radiocarbon reservoir age of south Florida during the Holocene","docAbstract":"A growing body of research suggests that the marine environments of south Florida provide a critical link between the tropical and high-latitude Atlantic. Changes in the characteristics of water masses off south Florida may therefore have important implications for our understanding of climatic and oceanographic variability over a broad spatial scale; however, the sources of variability within this oceanic corridor remain poorly understood. Measurements of ΔR, the local offset of the radiocarbon reservoir age, from shallow-water marine environments can serve as a powerful tracer of water-mass sources that can be used to reconstruct variability in local-to regional-scale oceanography and hydrology. We combined radiocarbon and U-series measurements of Holocene-aged corals from the shallow-water environments of the Florida Keys reef tract (FKRT) with robust statistical modeling to quantify the millennial-scale variability in ΔR at locations with (“nearshore”) and without (“open ocean”) substantial terrestrial influence. Our reconstructions demonstrate that there was significant spatial and temporal variability in ΔR on the FKRT during the Holocene. Whereas ΔR was similar throughout the region after ∼4000 years ago, nearshore ΔR was significantly higher than in the open ocean during the middle Holocene. We suggest that the elevated nearshore ΔR from ∼8000 to 5000 years ago was most likely the result of greater groundwater influence associated with lower sea level at this time. In the open ocean, which would have been isolated from the influence of groundwater, ΔR was lowest ∼7000 years ago, and was highest ∼3000 years ago. We evaluated our open-ocean model of ΔR variability against records of local-to regional-scale oceanography and conclude that local upwelling was not a significant driver of open-ocean radiocarbon variability in this region. Instead, the millennial-scale trends in open-ocean ΔR were more likely a result of broader-scale changes in western Atlantic circulation associated with an increase in the supply of equatorial South Atlantic water to the Caribbean and shifts in the character of South Atlantic waters resulting from variation in the intensity of upwelling off the southwest coast of Africa. Because accurate estimates of ΔR are critical to precise calibrations of radiocarbon dates from marine samples, we also developed models of nearshore and open-ocean ΔR versus conventional 14C ages that can be used for regional radiocarbon calibrations for the Holocene. Our study provides new insights into the patterns and drivers of oceanographic and hydrologic variability in the Straits of Florida and highlights the value of the paleoceanographic records from south Florida to our understanding of Holocene changes in climate and ocean circulation throughout the Atlantic.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quageo.2017.07.005","usgsCitation":"Toth, L., Cheng, H., Edwards, R., Ashe, E., and Richey, J.N., 2017, Millennial-scale variability in the local radiocarbon reservoir age of south Florida during the Holocene: Quaternary Geochronology, v. 42, p. 130-143, https://doi.org/10.1016/j.quageo.2017.07.005.","productDescription":"14 p.","startPage":"130","endPage":"143","ipdsId":"IP-084508","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":461385,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quageo.2017.07.005","text":"Publisher Index Page"},{"id":438188,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P8492Q","text":"USGS data release","linkHelpText":"Local Radiocarbon Reservoir Age (Delta-R) Variability from the Nearshore and Open-Ocean Environments of the Florida Keys Reef Tract During the Holocene and Associated U-Series and Radiocarbon Data"},{"id":346672,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.04290771484374,\n 24.492147541216028\n ],\n [\n -80.15625,\n 24.492147541216028\n ],\n [\n -80.15625,\n 25.535006795752302\n ],\n [\n -83.04290771484374,\n 25.535006795752302\n ],\n [\n -83.04290771484374,\n 24.492147541216028\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59e7168ee4b05fe04cd33178","contributors":{"authors":[{"text":"Toth, Lauren T. ltoth@usgs.gov","contributorId":149483,"corporation":false,"usgs":true,"family":"Toth","given":"Lauren T.","email":"ltoth@usgs.gov","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":712730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cheng, Hai","contributorId":85896,"corporation":false,"usgs":true,"family":"Cheng","given":"Hai","affiliations":[],"preferred":false,"id":712743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edwards, R. Lawrence","contributorId":55752,"corporation":false,"usgs":true,"family":"Edwards","given":"R. Lawrence","affiliations":[],"preferred":false,"id":712744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ashe, Erica","contributorId":194112,"corporation":false,"usgs":false,"family":"Ashe","given":"Erica","affiliations":[],"preferred":false,"id":712745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Richey, Julie N. 0000-0002-2319-7980 jrichey@usgs.gov","orcid":"https://orcid.org/0000-0002-2319-7980","contributorId":5182,"corporation":false,"usgs":true,"family":"Richey","given":"Julie","email":"jrichey@usgs.gov","middleInitial":"N.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":712746,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70191664,"text":"70191664 - 2017 - The importance of parameterization when simulating the hydrologic response of vegetative land-cover change","interactions":[],"lastModifiedDate":"2020-05-19T17:59:45.012244","indexId":"70191664","displayToPublicDate":"2017-10-17T00:00:00","publicationYear":"2017","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":"The importance of parameterization when simulating the hydrologic response of vegetative land-cover change","docAbstract":"Computer models of hydrologic systems are frequently used to investigate the hydrologic response of land-cover change. If the modeling results are used to inform resource-management decisions, then providing robust estimates of uncertainty in the simulated response is an important consideration. Here we examine the importance of parameterization, a necessarily subjective process, on uncertainty estimates of the simulated hydrologic response of land-cover change. Specifically, we applied the soil water assessment tool (SWAT) model to a 1.4 km2 watershed in southern Texas to investigate the simulated hydrologic response of brush management (the mechanical removal of woody plants), a discrete land-cover change. The watershed was instrumented before and after brush-management activities were undertaken, and estimates of precipitation, streamflow, and evapotranspiration (ET) are available; these data were used to condition and verify the model. The role of parameterization in brush-management simulation was evaluated by constructing two models, one with 12 adjustable parameters (reduced parameterization) and one with 1305 adjustable parameters (full parameterization). Both models were subjected to global sensitivity analysis as well as Monte Carlo and generalized likelihood uncertainty estimation (GLUE) conditioning to identify important model inputs and to estimate uncertainty in several quantities of interest related to brush management. Many realizations from both parameterizations were identified as behavioral
in that they reproduce daily mean streamflow acceptably well according to Nash–Sutcliffe model efficiency coefficient, percent bias, and coefficient of determination. However, the total volumetric ET difference resulting from simulated brush management remains highly uncertain after conditioning to daily mean streamflow, indicating that streamflow data alone are not sufficient to inform the model inputs that influence the simulated outcomes of brush management the most. Additionally, the reduced-parameterization model grossly underestimates uncertainty in the total volumetric ET difference compared to the full-parameterization model; total volumetric ET difference is a primary metric for evaluating the outcomes of brush management. The failure of the reduced-parameterization model to provide robust uncertainty estimates demonstrates the importance of parameterization when attempting to quantify uncertainty in land-cover change simulations.
Hydrologic recovery after wildfire is critical for restoring the ecosystem services of protecting of human lives and infrastructure from hazards and delivering water supply of sufficient quality and quantity. Recovery of soil-hydraulic properties, such as field-saturated hydraulic conductivity (Kfs), is a key factor for assessing the duration of watershed-scale flash flood and debris flow risks after wildfire. Despite the crucial role of Kfs in parameterizing numerical hydrologic models to predict the magnitude of postwildfire run-off and erosion, existing quantitative relations to predict Kfsrecovery with time since wildfire are lacking. Here, we conduct meta-analyses of 5 datasets from the literature that measure or estimate Kfs with time since wildfire for longer than 3-year duration. The meta-analyses focus on fitting 2 quantitative relations (linear and non-linear logistic) to explain trends in Kfs temporal recovery. The 2 relations adequately described temporal recovery except for 1 site where macropore flow dominated infiltration and Kfs recovery. This work also suggests that Kfs can have low hydrologic resistance (large postfire changes), and moderate to high hydrologic stability (recovery time relative to disturbance recurrence interval) and resilience (recovery of hydrologic function and provision of ecosystem services). Future Kfs relations could more explicitly incorporate processes such as soil-water repellency, ground cover and soil structure regeneration, macropore recovery, and vegetation regrowth.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11288","usgsCitation":"Ebel, B.A., and Martin, D.A., 2017, Meta-analysis of field-saturated hydraulic conductivity recovery following wildland fire: Applications for hydrologic model parameterization and resilience assessment: Hydrological Processes, v. 31, no. 21, p. 3682-3696, https://doi.org/10.1002/hyp.11288.","productDescription":"15 p.","startPage":"3682","endPage":"3696","ipdsId":"IP-084869","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":347327,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"21","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-30","publicationStatus":"PW","scienceBaseUri":"59f1a2a4e4b0220bbd9d9f34","contributors":{"authors":[{"text":"Ebel, Brian A. 0000-0002-5413-3963 bebel@usgs.gov","orcid":"https://orcid.org/0000-0002-5413-3963","contributorId":2557,"corporation":false,"usgs":true,"family":"Ebel","given":"Brian","email":"bebel@usgs.gov","middleInitial":"A.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":714984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Deborah A. 0000-0001-8237-0838 damartin@usgs.gov","orcid":"https://orcid.org/0000-0001-8237-0838","contributorId":168662,"corporation":false,"usgs":true,"family":"Martin","given":"Deborah","email":"damartin@usgs.gov","middleInitial":"A.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":714985,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70189991,"text":"ofr20171099 - 2017 - Watershed Data Management (WDM) database for West Branch DuPage River streamflow simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2013","interactions":[],"lastModifiedDate":"2017-10-16T13:43:05","indexId":"ofr20171099","displayToPublicDate":"2017-10-12T03:00:00","publicationYear":"2017","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":"2017-1099","title":"Watershed Data Management (WDM) database for West Branch DuPage River streamflow simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2013","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with the DuPage County Stormwater Management Department, maintains a database of hourly meteorological and hydrologic data for use in a near real-time streamflow simulation system. This system is used in the management and operation of reservoirs and other flood-control structures in the West Branch DuPage River watershed in DuPage County, Illinois. The majority of the precipitation data are collected from a tipping-bucket rain-gage network located in and near DuPage County. The other meteorological data (air temperature, dewpoint temperature, wind speed, and solar radiation) are collected at Argonne National Laboratory in Argonne, Ill. Potential evapotranspiration is computed from the meteorological data using the computer program LXPET (Lamoreux Potential Evapotranspiration). The hydrologic data (water-surface elevation [stage] and discharge) are collected at U.S.Geological Survey streamflow-gaging stations in and around DuPage County. These data are stored in a Watershed Data Management (WDM) database.
This report describes a version of the WDM database that is quality-assured and quality-controlled annually to ensure datasets are complete and accurate. This database is named WBDR13.WDM. It contains data from January 1, 2007, through September 30, 2013. Each precipitation dataset may have time periods of inaccurate data. This report describes the methods used to estimate the data for the periods of missing, erroneous, or snowfall-affected data and thereby improve the accuracy of these data. The other meteorological datasets are described in detail in Over and others (2010), and the hydrologic datasets in the database are fully described in the online USGS annual water data reports for Illinois (U.S. Geological Survey, 2016) and, therefore, are described in less detail than the precipitation datasets in this report.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171099","collaboration":"Prepared in cooperation with DuPage County Stormwater Management Department","usgsCitation":"Bera, Maitreyee, 2017, Watershed Data Management (WDM) database for West Branch DuPage River streamflow simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2013: U.S. Geological Survey Open-File Report 2017–1099, 39 p., https://doi.org/10.3133/ofr20171099.","productDescription":"Report: v, 39 p.; Data Release","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-078980","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":346557,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1099/ofr20171099.pdf","text":"Report","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1099"},{"id":346556,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1099/coverthb.jpg"},{"id":346558,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71Z42M0","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"Watershed Data Management (WDM) Database (WBDR13.WDM) for West Branch DuPage River Streamflow Simulation, DuPage County, Illinois, January 1, 2007, through September 30, 2013"}],"country":"United States","state":"Illinois","county":"DuPage County","otherGeospatial":"West Branch DuPage River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.28338623046875,\n 41.70214000452559\n ],\n [\n -87.99087524414062,\n 41.70214000452559\n ],\n [\n -87.99087524414062,\n 41.99726342796974\n ],\n [\n -88.28338623046875,\n 41.99726342796974\n ],\n [\n -88.28338623046875,\n 41.70214000452559\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801-2347
The consequence of a 1996 wildfire disturbance and a subsequent high-intensity summer convective rain storm (about 110 millimeters per hour) was the deposition of a sediment superslug in the Spring Creek basin (26.8 square kilometers) of the Front Range Mountains in Colorado. Spring Creek is a tributary to the South Platte River upstream from Strontia Springs Reservoir, which supplies domestic water for the cities of Denver and Aurora. Changes in a superslug were monitored over the course of 18 years (1996–2014) by repeat surveys at 18 channel cross sections spaced at nearly equal intervals along a 1,500-meter study reach and by a time series of photographs of each cross section. Surveys were not repeated at regular time intervals but after major changes caused by different geomorphic processes. The focus of this long-term study was to understand the evolution and internal alluvial architecture of chronostratigraphic units (defined as the volume of sediment deposited between two successive surveys), and the preservation or storage of these units in the superslug. The data are presented as a series of 18 narratives (one for each cross section) that summarize the changes, illustrate these changes with photographs, and provide a preservation plot showing the amount of each chronostratigraphic unit still remaining in June 2014.
The most significant hydrologic change after the wildfire was an exponential decrease in peak discharge of flash floods caused by summer convective rain storms. In response to these hydrologic changes, all 18 locations went through an aggradation phase, an incision phase, and finally a stabilization phase. However, the architecture of the chronostratigraphic units differs from cross section to cross section, and units are characterized by either a laminar, fragmented, or hybrid alluvial architecture. In response to the decrease in peak-flood discharge and the increase in hillslope and riparian vegetation, Spring Creek abandoned many of the nearly horizontal erosional and depositional surfaces and left a landscape consisting of a series of cut-and-fill terraces as a legacy of this wildfire disturbance.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171090","usgsCitation":"Moody, J.A., and Martin, D.A., 2017, Description of chronostratigraphic units preserved as channel deposits and geomorphic processes following a basin-scale disturbance by a wildfire in Colorado: U.S. Geological Survey Open-File Report 2017–1090, 73 p., https://doi.org/10.3133/ofr20171090.","productDescription":"vi, 73 p.","numberOfPages":"79","onlineOnly":"Y","ipdsId":"IP-081971","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":346458,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1090/coverthb.jpg"},{"id":346459,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1090/ofr20171090.pdf","text":"Report","size":"43.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1090"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.0455322265625,\n 38.89958342598271\n ],\n [\n -104.600830078125,\n 38.89958342598271\n ],\n [\n -104.600830078125,\n 39.50827899034114\n ],\n [\n -106.0455322265625,\n 39.50827899034114\n ],\n [\n -106.0455322265625,\n 38.89958342598271\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Branch Chief, Hydrodynamics Branch
Earth System Processes Division
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Climate change raises concern that risks of hydrological drought may be increasing. We estimate hydrological drought probabilities for rivers and streams in the United States (U.S.) using maximum likelihood logistic regression (MLLR). Streamflow data from winter months are used to estimate the chance of hydrological drought during summer months. Daily streamflow data collected from 9,144 stream gages from January 1, 1884 through January 9, 2014 provide hydrological drought streamflow probabilities for July, August, and September as functions of streamflows during October, November, December, January, and February, estimating outcomes 5-11 months ahead of their occurrence. Few drought prediction methods exploit temporal links among streamflows. We find MLLR modeling of drought streamflow probabilities exploits the explanatory power of temporally linked water flows. MLLR models with strong correct classification rates were produced for streams throughout the U.S. One ad hoc test of correct prediction rates of September 2013 hydrological droughts exceeded 90% correct classification. Some of the best-performing models coincide with areas of high concern including the West, the Midwest, Texas, the Southeast, and the Mid-Atlantic. Using hydrological drought MLLR probability estimates in a water management context can inform understanding of drought streamflow conditions, provide warning of future drought conditions, and aid water management decision making.
","language":"English","publisher":"Wiley","doi":"10.1111/1752-1688.12562","usgsCitation":"Austin, S.H., and Nelms, D.L., 2017, Modeling summer month hydrological drought probabilities in the United States using antecedent flow conditions: Journal of the American Water Resources Association, v. 53, no. 5, p. 1133-1146, https://doi.org/10.1111/1752-1688.12562.","productDescription":"14 p.","startPage":"1133","endPage":"1146","ipdsId":"IP-069502","costCenters":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"links":[{"id":469450,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.12562","text":"Publisher Index Page"},{"id":438190,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7HH6H8H","text":"USGS data release","linkHelpText":"Terms, Statistics, and Performance Measures for Maximum Likelihood Logistic Regression Models Estimating Hydrological Drought Probabilities in 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Energy‐budget evaporation was determined during approximately biweekly periods at\na northern Minnesota, USA, lake for 5 years. Various combinations of shortwave radiation, air\ntemperature, wind speed, lake‐surface temperature, and vapour‐pressure difference were\nrelated to energy‐budget evaporation using linear‐regression models in an effort to determine\ndaily evaporation without requiring the heat‐storage term. The model that combined the product\nof shortwave radiation and air temperature with the product of vapour‐pressure difference\nand wind speed provided the second best fit based on statistics but provided the best daily\ndata based on comparisons with evaporation determined with the eddy‐covariance method.\nBest‐model daily values ranged from −0.6 to 7.1 mm/day over a 5‐year period. Daily averages\nof best‐model evaporation and eddy‐covariance evaporation were nearly identical for all\n28 days of comparisons with a standard deviation of the differences between the two\nmethods of 0.68 mm/day. Best‐model daily evaporation also was compared with two other\nevaporation models, Jensen–Haise and a mass‐transfer model. Best‐model daily values were\nsubstantially improved relative to Jensen–Haise and mass‐transfer values when daily values\nwere summed over biweekly energy‐budget periods for comparison with energy‐budget\nresults.","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11375","usgsCitation":"Andreasen, M., Rosenberry, D.O., and Stannard, D., 2017, Estimating daily lake evaporation from biweekly energy‐budget data: Hydrological Processes, v. 31, no. 25, p. 4530-4539, https://doi.org/10.1002/hyp.11375.","productDescription":"10 p.","startPage":"4530","endPage":"4539","ipdsId":"IP-051452","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":369472,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Northern Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.78955078125,\n 46.89023157359399\n ],\n [\n -92.21923828124999,\n 46.437856895024204\n ],\n [\n -89.3408203125,\n 48.03401915864286\n ],\n [\n -92.46093749999999,\n 48.63290858589535\n ],\n [\n -94.39453125,\n 48.80686346108517\n ],\n [\n -94.85595703125,\n 49.48240137826932\n ],\n [\n -95.33935546875,\n 49.48240137826932\n ],\n [\n -95.3173828125,\n 49.023461463214126\n ],\n [\n -97.3388671875,\n 49.081062364320736\n ],\n [\n -96.78955078125,\n 46.89023157359399\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"25","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Andreasen, Mie 0000-0002-5661-1359","orcid":"https://orcid.org/0000-0002-5661-1359","contributorId":220835,"corporation":false,"usgs":false,"family":"Andreasen","given":"Mie","email":"","affiliations":[{"id":40283,"text":"University of Copenhagen, Denmark","active":true,"usgs":false}],"preferred":false,"id":775879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rosenberry, Donald O. 0000-0003-0681-5641 rosenber@usgs.gov","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":1312,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald","email":"rosenber@usgs.gov","middleInitial":"O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":775878,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stannard, David distanna@usgs.gov","contributorId":220836,"corporation":false,"usgs":true,"family":"Stannard","given":"David","email":"distanna@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":775880,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70188270,"text":"tm6B8 - 2017 - Documentation of the dynamic parameter, water-use, stream and lake flow routing, and two summary output modules and updates to surface-depression storage simulation and initial conditions specification options with the Precipitation-Runoff Modeling System (PRMS)","interactions":[],"lastModifiedDate":"2017-10-05T11:31:16","indexId":"tm6B8","displayToPublicDate":"2017-10-05T09:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-B8","title":"Documentation of the dynamic parameter, water-use, stream and lake flow routing, and two summary output modules and updates to surface-depression storage simulation and initial conditions specification options with the Precipitation-Runoff Modeling System (PRMS)","docAbstract":"This report documents seven enhancements to the U.S. Geological Survey (USGS) Precipitation-Runoff Modeling System (PRMS) hydrologic simulation code: two time-series input options, two new output options, and three updates of existing capabilities. The enhancements are (1) new dynamic parameter module, (2) new water-use module, (3) new Hydrologic Response Unit (HRU) summary output module, (4) new basin variables summary output module, (5) new stream and lake flow routing module, (6) update to surface-depression storage and flow simulation, and (7) update to the initial-conditions specification. This report relies heavily upon U.S. Geological Survey Techniques and Methods, book 6, chapter B7, which documents PRMS version 4 (PRMS-IV). A brief description of PRMS is included in this report.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section B: Surface water in Book 6: Modeling techniques","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm6B8","collaboration":"National Water Census, Water Availability and Use Science Program","usgsCitation":"Regan, R.S., and LaFontaine, J.H., 2017, Documentation of the dynamic parameter, water-use, stream and lake flow routing, and two summary output modules and updates to surface-depression storage simulation and initial conditions specification options with the Precipitation-Runoff Modeling System (PRMS): U.S. Geological Survey Techniques and Methods, book 6, chap. B8, 60 p., https://doi.org/10.3133/tm6B8.","productDescription":"Report: ix, 60 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-075744","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":346255,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7XG9PCF","text":"USGS data release","description":"USGS data release","linkHelpText":"Model Input and Output for Hydrologic Simulations of the Upper Chattahoochee River Basin that Demonstrate Enhancements to the Precipitation Runoff Modeling System"},{"id":346254,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/06/b8/tm6b8.pdf","text":"Report","size":"3.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 6-B8"},{"id":346253,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/06/b8/coverthb.jpg"}],"publicComments":"This report is Chapter 8 of Section B: Surface water in Book 6: Modeling techniques","contact":"Director
U.S. Geological Survey, South Atlantic Water Science Center
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
(803) 750-6100
https://www.usgs.gov/centers/sa-water
Many large rivers around the world no longer flow to their deltas, due to ever greater water withdrawals and diversions for human needs. However, the importance of riparian ecosystems is drawing increasing recognition, leading to the allocation of environmental flows to restore river processes. Accurate estimates of riparian plant evapotranspiration (ET) are needed to understand how the riverine system responds to these rare events and achieve the goals of environmental flows. In 2014, historic environmental flows were released into the Lower Colorado River at Morelos Dam (Mexico); this once perennial but now dry reach is the final stretch to the mighty Colorado River Delta. One of the primary goals was to supply native vegetation restoration sites along the reach with water to help seedlings establish and boost groundwater levels to foster the planted saplings. Patterns in ET before, during, and after the flows are useful for evaluating whether this goal was met and understanding the role that ET plays in this now ephemeral river system. Here, diurnal fluctuations in groundwater levels and MODIS data were used to compare estimates of ET specifically at three native vegetation restoration sites during 2014 planned flow events, while MODIS data was used to evaluate long-term (2002 – 2016) ET responses to restoration efforts at these sites. Overall, ET was generally 0 - 10 mm d-1 across sites and although daily ET values from groundwater data were highly variable, weekly averaged estimates were highly correlated with MODIS-derived estimates at most sites. The influence of the 2014 flow events was not immediately apparent in the results, although the process of clearing vegetation and planting native vegetation at the restoration sites was clearly visible in the results.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.11359","usgsCitation":"Shanafield, M., Jurado, H.G., Burgueno, J.E., Hernandez, J.R., Jarchow, C., and Nagler, P.L., 2017, Short-term and long-term evapotranspiration rates at ecological restoration sites along a large river receiving rare flow events: Hydrological Processes, v. 31, no. 24, p. 4328-4337, https://doi.org/10.1002/hyp.11359.","productDescription":"10 p.","startPage":"4328","endPage":"4337","ipdsId":"IP-068603","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":469457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.11359","text":"Publisher Index Page"},{"id":346385,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.08453369140625,\n 32.217448573031014\n ],\n [\n -114.63821411132812,\n 32.217448573031014\n ],\n [\n -114.63821411132812,\n 32.751477587458865\n ],\n [\n -115.08453369140625,\n 32.751477587458865\n ],\n [\n -115.08453369140625,\n 32.217448573031014\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"24","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-17","publicationStatus":"PW","scienceBaseUri":"59d5f342e4b05fe04cc652b8","contributors":{"authors":[{"text":"Shanafield, Margaret","contributorId":196916,"corporation":false,"usgs":false,"family":"Shanafield","given":"Margaret","email":"","affiliations":[],"preferred":false,"id":711930,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jurado, Hugo Gutierrez","contributorId":196917,"corporation":false,"usgs":false,"family":"Jurado","given":"Hugo","email":"","middleInitial":"Gutierrez","affiliations":[],"preferred":false,"id":711931,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burgueno, Jesus Eliana Rodriguez","contributorId":196918,"corporation":false,"usgs":false,"family":"Burgueno","given":"Jesus","email":"","middleInitial":"Eliana Rodriguez","affiliations":[],"preferred":false,"id":711932,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hernandez, Jorge Ramirez","contributorId":196919,"corporation":false,"usgs":false,"family":"Hernandez","given":"Jorge","email":"","middleInitial":"Ramirez","affiliations":[],"preferred":false,"id":711933,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jarchow, Christopher 0000-0002-0424-4104 cjarchow@usgs.gov","orcid":"https://orcid.org/0000-0002-0424-4104","contributorId":196069,"corporation":false,"usgs":true,"family":"Jarchow","given":"Christopher","email":"cjarchow@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":711928,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nagler, Pamela L. 0000-0003-0674-103X pnagler@usgs.gov","orcid":"https://orcid.org/0000-0003-0674-103X","contributorId":1398,"corporation":false,"usgs":true,"family":"Nagler","given":"Pamela","email":"pnagler@usgs.gov","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":711927,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70191285,"text":"70191285 - 2017 - Science advancements key to increasing management value of life stage monitoring networks for endangered Sacramento River winter-run Chinook salmon in California","interactions":[],"lastModifiedDate":"2017-10-03T14:20:42","indexId":"70191285","displayToPublicDate":"2017-10-03T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Science advancements key to increasing management value of life stage monitoring networks for endangered Sacramento River winter-run Chinook salmon in California","docAbstract":"A robust monitoring network that provides quantitative information about the status of imperiled species at key life stages and geographic locations over time is fundamental for sustainable management of fisheries resources. For anadromous species, management actions in one geographic domain can substantially affect abundance of subsequent life stages that span broad geographic regions. Quantitative metrics (e.g., abundance, movement, survival, life history diversity, and condition) at multiple life stages are needed to inform how management actions (e.g., hatcheries, harvest, hydrology, and habitat restoration) influence salmon population dynamics. The existing monitoring network for endangered Sacramento River winterrun Chinook Salmon (SRWRC, Oncorhynchus tshawytscha) in California’s Central Valley was compared to conceptual models developed for each life stage and geographic region of the life cycle to identify relevant SRWRC metrics. We concluded that the current monitoring network was insufficient to diagnose when (life stage) and where (geographic domain) chronic or episodic reductions in SRWRC cohorts occur, precluding within- and among-year comparisons. The strongest quantitative data exist in the Upper Sacramento River, where abundance estimates are generated for adult spawners and emigrating juveniles. However, once SRWRC leave the upper river, our knowledge of their identity, abundance, and condition diminishes, despite the juvenile monitoring enterprise. We identified six system-wide recommended actions to strengthen the value of data generated from the existing monitoring network to assess resource management actions: (1) incorporate genetic run identification; (2) develop juvenile abundance estimates; (3) collect data for life history diversity metrics at multiple life stages; (4) expand and enhance real-time fish survival and movement monitoring; (5) collect fish condition data; and (6) provide timely public access to monitoring data in open data formats. To illustrate how updated technologies can enhance the existing monitoring to provide quantitative data on SRWRC, we provide examples of how each recommendation can address specific management issues.
","language":"English","publisher":"Delta Science Program and the UC Davis John Muir Instutute of the Environment","doi":"10.15447/sfews.2017v15iss3art1","usgsCitation":"Johnson, R.C., Windell, S., Brandes, P.L., Conrad, J.L., Ferguson, J., Goertler, P.A., Harvey, B.N., Heublein, J., Isreal, J.A., Kratville, D.W., Kirsch, J.E., Perry, R.W., Pisciotto, J., Poytress, W.R., Reece, K., and Swart, B.G., 2017, Science advancements key to increasing management value of life stage monitoring networks for endangered Sacramento River winter-run Chinook salmon in California: San Francisco Estuary and Watershed Science, v. 15, no. 3, p. 1-41, https://doi.org/10.15447/sfews.2017v15iss3art1.","productDescription":"Article 1; 41 p.","startPage":"1","endPage":"41","ipdsId":"IP-076305","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":469459,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2017v15iss3art1","text":"Publisher Index Page"},{"id":346362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.6019287109375,\n 37.400710068740565\n ],\n [\n -121.39343261718749,\n 37.400710068740565\n ],\n [\n -121.39343261718749,\n 40.643135583312805\n ],\n [\n -122.6019287109375,\n 40.643135583312805\n ],\n [\n -122.6019287109375,\n 37.400710068740565\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-27","publicationStatus":"PW","scienceBaseUri":"59d4a1a2e4b05fe04cc4e0d6","contributors":{"authors":[{"text":"Johnson, Rachel C.","contributorId":196877,"corporation":false,"usgs":false,"family":"Johnson","given":"Rachel","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":711845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windell, Sean","contributorId":196878,"corporation":false,"usgs":false,"family":"Windell","given":"Sean","email":"","affiliations":[],"preferred":false,"id":711846,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandes, Patricia L.","contributorId":196879,"corporation":false,"usgs":false,"family":"Brandes","given":"Patricia","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":711847,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Conrad, J. Louise","contributorId":196880,"corporation":false,"usgs":false,"family":"Conrad","given":"J.","email":"","middleInitial":"Louise","affiliations":[],"preferred":false,"id":711848,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ferguson, John","contributorId":196881,"corporation":false,"usgs":false,"family":"Ferguson","given":"John","affiliations":[],"preferred":false,"id":711849,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goertler, Pascale A. L.","contributorId":196882,"corporation":false,"usgs":false,"family":"Goertler","given":"Pascale","email":"","middleInitial":"A. L.","affiliations":[],"preferred":false,"id":711850,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Harvey, Brett N.","contributorId":196883,"corporation":false,"usgs":false,"family":"Harvey","given":"Brett","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":711851,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Heublein, Joseph","contributorId":196884,"corporation":false,"usgs":false,"family":"Heublein","given":"Joseph","email":"","affiliations":[],"preferred":false,"id":711852,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Isreal, Joshua A.","contributorId":196885,"corporation":false,"usgs":false,"family":"Isreal","given":"Joshua","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":711853,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kratville, Daniel W.","contributorId":196892,"corporation":false,"usgs":false,"family":"Kratville","given":"Daniel","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":711864,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kirsch, Joseph E.","contributorId":171939,"corporation":false,"usgs":false,"family":"Kirsch","given":"Joseph","email":"","middleInitial":"E.","affiliations":[{"id":5128,"text":"U.S. Fish and Wildlife Service, University of Montana, Missoula, MT 59812","active":true,"usgs":false}],"preferred":false,"id":711854,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":711844,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Pisciotto, Joseph","contributorId":196886,"corporation":false,"usgs":false,"family":"Pisciotto","given":"Joseph","email":"","affiliations":[],"preferred":false,"id":711856,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Poytress, William R.","contributorId":196887,"corporation":false,"usgs":false,"family":"Poytress","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711857,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Reece, Kevin","contributorId":196888,"corporation":false,"usgs":false,"family":"Reece","given":"Kevin","email":"","affiliations":[],"preferred":false,"id":711858,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Swart, Brycen G.","contributorId":196889,"corporation":false,"usgs":false,"family":"Swart","given":"Brycen","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":711859,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70191268,"text":"70191268 - 2017 - Model parameters for representative wetland plant functional groups","interactions":[],"lastModifiedDate":"2017-10-08T12:16:12","indexId":"70191268","displayToPublicDate":"2017-10-02T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Model parameters for representative wetland plant functional groups","docAbstract":"Wetlands provide a wide variety of ecosystem services including water quality remediation, biodiversity refugia, groundwater recharge, and floodwater storage. Realistic estimation of ecosystem service benefits associated with wetlands requires reasonable simulation of the hydrology of each site and realistic simulation of the upland and wetland plant growth cycles. Objectives of this study were to quantify leaf area index (LAI), light extinction coefficient (k), and plant nitrogen (N), phosphorus (P), and potassium (K) concentrations in natural stands of representative plant species for some major plant functional groups in the United States. Functional groups in this study were based on these parameters and plant growth types to enable process-based modeling. We collected data at four locations representing some of the main wetland regions of the United States. At each site, we collected on-the-ground measurements of fraction of light intercepted, LAI, and dry matter within the 2013–2015 growing seasons. Maximum LAI and k variables showed noticeable variations among sites and years, while overall averages and functional group averages give useful estimates for multisite simulation modeling. Variation within each species gives an indication of what can be expected in such natural ecosystems. For P and K, the concentrations from highest to lowest were spikerush (Eleocharis macrostachya), reed canary grass (Phalaris arundinacea), smartweed (Polygonum spp.), cattail (Typha spp.), and hardstem bulrush (Schoenoplectus acutus). Spikerush had the highest N concentration, followed by smartweed, bulrush, reed canary grass, and then cattail. These parameters will be useful for the actual wetland species measured and for the wetland plant functional groups they represent. These parameters and the associated process-based models offer promise as valuable tools for evaluating environmental benefits of wetlands and for evaluating impacts of various agronomic practices in adjacent areas as they affect wetlands.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1958","usgsCitation":"Williams, A.S., Kiniry, J.R., Mushet, D.M., Smith, L., McMurry, S.T., Attebury, K., Lang, M., McCarty, G.W., Shaffer, J.A., Effland, W.R., and Johnson, M., 2017, Model parameters for representative wetland plant functional groups: Ecosphere, v. 8, no. 10, p. 1-14, https://doi.org/10.1002/ecs2.1958.","productDescription":"Article e01958; 14 p.","startPage":"1","endPage":"14","ipdsId":"IP-075940","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469465,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1958","text":"Publisher Index Page"},{"id":346339,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"10","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-02","publicationStatus":"PW","scienceBaseUri":"59d35025e4b05fe04cc34d45","contributors":{"authors":[{"text":"Williams, Amber S.","contributorId":196855,"corporation":false,"usgs":false,"family":"Williams","given":"Amber","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":711793,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kiniry, James R.","contributorId":66918,"corporation":false,"usgs":true,"family":"Kiniry","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711794,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":711795,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Loren M.","contributorId":88876,"corporation":false,"usgs":true,"family":"Smith","given":"Loren M.","affiliations":[],"preferred":false,"id":711796,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McMurry, Scott T.","contributorId":191876,"corporation":false,"usgs":false,"family":"McMurry","given":"Scott","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":711797,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Attebury, Kelly","contributorId":196857,"corporation":false,"usgs":false,"family":"Attebury","given":"Kelly","email":"","affiliations":[],"preferred":false,"id":711798,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lang, Megan","contributorId":156431,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","affiliations":[{"id":7261,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD, 20742","active":true,"usgs":false}],"preferred":false,"id":711799,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCarty, Gregory W.","contributorId":192367,"corporation":false,"usgs":false,"family":"McCarty","given":"Gregory","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":711800,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shaffer, Jill A. 0000-0003-3172-0708 jshaffer@usgs.gov","orcid":"https://orcid.org/0000-0003-3172-0708","contributorId":3184,"corporation":false,"usgs":true,"family":"Shaffer","given":"Jill","email":"jshaffer@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":711801,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Effland, William R.","contributorId":196858,"corporation":false,"usgs":false,"family":"Effland","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":711802,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Johnson, Mari-Vaughn V.","contributorId":196859,"corporation":false,"usgs":false,"family":"Johnson","given":"Mari-Vaughn V.","affiliations":[],"preferred":false,"id":711803,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70200587,"text":"70200587 - 2017 - Irrigation as a fuel pump to freshwater ecosystems","interactions":[],"lastModifiedDate":"2018-10-25T11:32:46","indexId":"70200587","displayToPublicDate":"2017-10-01T11:32:39","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1007,"text":"Biogeochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Irrigation as a fuel pump to freshwater ecosystems","docAbstract":"We generated a detailed time series of total dissolved hydrolyzable amino acids (DHAA) in a watershed dominated by irrigated agriculture in northern California, USA to investigate the roles of hydrologic and seasonal changes on the composition of dissolved organic matter (DOM). DHAA are sensitive indicators of the degradation state and reactivity of DOM. DHAA concentrations ranged from 0.55 to 9.96 μM (median 3.51 ± 1.80 μM), with expected peaks during high-discharge storms and unexpected high values throughout the low-discharge irrigation season. Overall, summer irrigation was a critical hydrologic regime for DOM cycling since it mobilized DOM similar in concentration and reactivity to DOM released during storms. Together, irrigation and storm flows exported DOM with (1) the largest DHAA contributions to the dissolved organic carbon and the dissolved organic nitrogen pools, (2) the largest proportion of basic amino acids, and (3) the lowest degradation extent based on multiple indices. In this highly disturbed terrestrial system, UV–vis absorbance did not correlate with DHAA concentrations, while classic interpretations of common amino acid indicators (e.g., proportion of basic amino acids, degradation index, percent of non-protein amino acids) were prone to conflicting characterizations of DOM reactivity. Therefore, a new parameter (processing ratio, PR) derived from individual amino acid concentrations was developed that demonstrated a strong potential for mechanistic-driven characterization of the extent of DOM diagenesis in freshwaters. Irrigated agriculture altered stream biogeochemistry by releasing a continuous supply of reactive DOM (lowest PR values), thereby providing an additional energy source to downstream ecosystems.
","language":"English","publisher":"Springer","doi":"10.1007/s10533-017-0381-2","usgsCitation":"Matiasek, S., Pellerin, B., Spencer, R., Bergamaschi, B.A., and Hernes, P.J., 2017, Irrigation as a fuel pump to freshwater ecosystems: Biogeochemistry, v. 136, no. 1, p. 71-90, https://doi.org/10.1007/s10533-017-0381-2.","productDescription":"20 p.","startPage":"71","endPage":"90","ipdsId":"IP-086392","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":358801,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Willow Slough watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.25,\n 38.5\n ],\n [\n -121.6667,\n 38.5\n ],\n [\n -121.6667,\n 38.75\n ],\n [\n -122.25,\n 38.75\n ],\n [\n -122.25,\n 38.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"136","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-26","publicationStatus":"PW","scienceBaseUri":"5c10ab02e4b034bf6a7e5f3f","contributors":{"authors":[{"text":"Matiasek, Sandrine J. 0000-0003-0272-0354","orcid":"https://orcid.org/0000-0003-0272-0354","contributorId":210031,"corporation":false,"usgs":false,"family":"Matiasek","given":"Sandrine","middleInitial":"J.","affiliations":[{"id":38054,"text":"Department of Geological and Environmental Sciences, California State University Chico, 400 W 1st St, Chico, CA 95929, USA","active":true,"usgs":false}],"preferred":false,"id":749652,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pellerin, Brian A. 0000-0003-3712-7884","orcid":"https://orcid.org/0000-0003-3712-7884","contributorId":204324,"corporation":false,"usgs":true,"family":"Pellerin","given":"Brian A.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":749651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spencer, Robert G.M.","contributorId":173304,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert G.M.","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":749653,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bergamaschi, Brian A. 0000-0002-9610-5581 bbergama@usgs.gov","orcid":"https://orcid.org/0000-0002-9610-5581","contributorId":140776,"corporation":false,"usgs":true,"family":"Bergamaschi","given":"Brian","email":"bbergama@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":749654,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hernes, Peter J.","contributorId":139730,"corporation":false,"usgs":false,"family":"Hernes","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":12894,"text":"Department of Land, Air, and Water Resources, University of California, One Shields Avenue, Davis, CA, 95616, USA","active":true,"usgs":false}],"preferred":false,"id":749655,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}