The unsaturated zone (UZ) extends across the Earth’s terrestrial surface and is central to many problems related to land and water resource management. Flow of water through the UZ is typically thought to be slow and diffusive, such that it could attenuate fluxes and dampen variability between atmospheric inputs and underlying aquifer systems. This would reduce water resource vulnerability to contaminants and water-related hazards. Reducing or negating that effect, however, spatially concentrated and rapid flow and transport through the unsaturated zone is surprisingly common and becoming more so with the increasing frequency and magnitude of extreme hydroclimatic events. Arising from the wide range in the rates and complex modes of nonlinear flow processes, these effects are among the most poorly characterized hydrologic phenomena. Issues of scale present additional difficulties. Equations representing unsaturated processes have been developed and tested on the basis of field and laboratory measurements typically made at scales from pore size to plot size. In contrast, related problems of significant interest to society, including floods, aquifer recharge, landslides, and groundwater contamination, range from watershed to regional scales. The disparity between the scale of our understanding and the scale of interest for societal problems has spurred application of these model equations at increasingly coarse resolutions over larger areas than can be justified by existing measurements or theory. This mismatch in scales requires an assumption that spatially averaging slow diffusive flow and rapid preferential flow can effectively represent the influence of both processes across vast areas. Given the currently inadequate recognition and quantitative characterization of focused and rapid processes in unsaturated flow, these phenomena are critically in need of expanded attention and effort.
A key assumption in species distribution modeling (SDM) with presence‐background (PB) methods is that sampling of occurrence localities is unbiased and that any sampling bias is proportional to the background distribution of environmental covariates. This assumption is rarely met when SDM practitioners rely on federated museum records from natural history collections for geo‐located occurrences due to inherent sampling bias found in these collections. We use a simulation approach to explore the effectiveness of three methods developed to account for sampling bias in SDM with PB frameworks. Two of the methods rely on careful filtering of observation data—geographic thinning (G‐Filter) and environmental thinning (E‐Filter)—while a third, FactorBiasOut, creates selection weights for background data to bias locations toward areas where the observation dataset was sampled. While these methods have been assessed previously, evaluation has emphasized spatial predictions of habitat potential. Here, we dig deeper into the effectiveness of these methods by exploring how sampling bias not only affects predictions of habitat potential, but also our understanding of niche characteristics such as which explanatory variables and response curves best represent species–environment relationships. We simulate 100 virtual species ranging from generalist to specialist in their habitat preferences and introduce geographic and environmental bias at three intensity levels to measure the effectiveness of each correction method to (1) predict true probability of occurrence across a study area, (2) recover true species–environment relationships, and (3) identify true explanatory variables. We find that the FactorBiasOut most often showed the greatest improvement in recreating known distributions but did no better at correctly identifying environmental covariates or recreating species–environment relationships than G‐Filter or E‐Filter methods. Narrow niche species are most problematic for biased calibration datasets, such that correction methods can, in some cases, make predictions worse.
Despite the importance of species–area relationships (SARs) to conservation, SARs in human‐fragmented rivers have received little attention. Our aim was to test for the presence and strength of SARs for littoral fish assemblages of an extensively dammed river in south‐central Ontario, Canada, and to examine long‐running hypotheses for the drivers of SARs. Twenty‐six navigational dams with locks built between 1837 and 1913 occur along the 160 km length of the Trent River examined in this study. We evaluated the relationship between richness and fragment area, and then used linear models to test whether the area per se, habitat diversity, or other hypotheses were best supported by the data. A power–function relationship with area explained 46% of the variation in fish species richness, and the slope (z = 0.4) was high compared with SARs reported from other ecosystems, indicating that species accumulated rapidly with an increase in fragment area. Multi‐predictor models suggested that area was significantly related to richness, but that vegetation cover diversity had a stronger relative effect. The slope of our SAR may indicate that there is a high degree of isolation between populations in different fragments, even though the lock system reportedly allows some passage of organisms. Our findings also suggest that mitigating against local extinction due to small population sizes (i.e., area effects), and enhancing aquatic vegetation cover may be viable strategies for promoting species diversity in the study river. Studies of SARs in fragmented rivers may offer additional benefits to supporting restoration planning where efforts are being made to increase species diversity.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3411","usgsCitation":"Carl, L.M., Esselman, P., Sparks-Jackson, B.L., and Wilson, C.C., 2021, The species–area relationship for a highly fragmented temperate river system: Ecosphere, v. 12, no. 3, e03411, 17 p., https://doi.org/10.1002/ecs2.3411.","productDescription":"e03411, 17 p.","ipdsId":"IP-074620","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":490069,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3411","text":"Publisher Index Page"},{"id":436433,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L1AYLW","text":"USGS data release","linkHelpText":"Habitat and fish assemblages along four river mainstems in Ontario, Canada, 1997 to 2001, with supporting spatial data"},{"id":384698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada","state":"Ontario","otherGeospatial":"Rice Lake, Trent River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.99468994140625,\n 44.43476045009948\n ],\n [\n -78.41629028320312,\n 44.173339873464684\n ],\n [\n -78.28582763671875,\n 43.94537239244209\n ],\n [\n -78.01803588867188,\n 43.95822503841972\n ],\n [\n -77.86972045898438,\n 43.982933852960805\n ],\n [\n -77.73101806640625,\n 43.982933852960805\n ],\n [\n -77.57858276367188,\n 44.049102784014536\n ],\n [\n -77.574462890625,\n 44.26683800273895\n ],\n [\n -77.99468994140625,\n 44.43476045009948\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Carl, Leon M. 0000-0001-6419-2214 lcarl@usgs.gov","orcid":"https://orcid.org/0000-0001-6419-2214","contributorId":256693,"corporation":false,"usgs":true,"family":"Carl","given":"Leon","email":"lcarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":5068,"text":"Midwest Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":813065,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":813066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sparks-Jackson, Beth L 0000-0002-1726-1480","orcid":"https://orcid.org/0000-0002-1726-1480","contributorId":256695,"corporation":false,"usgs":false,"family":"Sparks-Jackson","given":"Beth","email":"","middleInitial":"L","affiliations":[{"id":51831,"text":"Contractor to USGS Great Lakes Science Center","active":true,"usgs":false}],"preferred":false,"id":813067,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Christopher C. 0000-0002-9528-0652","orcid":"https://orcid.org/0000-0002-9528-0652","contributorId":256696,"corporation":false,"usgs":false,"family":"Wilson","given":"Christopher","email":"","middleInitial":"C.","affiliations":[{"id":51832,"text":"Aquatic Biodiversity and Conservation Unit, Ontario Ministry of Natural Resources, Peterborough, ON, Canada","active":true,"usgs":false}],"preferred":false,"id":813068,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219125,"text":"sir20205140 - 2021 - Evaluation and application of the Purge Analyzer Tool (PAT) to determine in-well flow and purge criteria for sampling monitoring wells at the Stringfellow Superfund site in Jurupa Valley, California, in 2017","interactions":[],"lastModifiedDate":"2021-03-25T15:53:32.129298","indexId":"sir20205140","displayToPublicDate":"2021-03-25T09:45:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5140","displayTitle":"Evaluation and Application of the Purge Analyzer Tool (PAT) To Determine In-Well Flow and Purge Criteria for Sampling Monitoring Wells at the Stringfellow Superfund Site in Jurupa Valley, California, in 2017","title":"Evaluation and application of the Purge Analyzer Tool (PAT) to determine in-well flow and purge criteria for sampling monitoring wells at the Stringfellow Superfund site in Jurupa Valley, California, in 2017","docAbstract":"The U.S. Geological Survey and U.S. Environmental Protection Agency are developing analytical tools to assess the representativeness of groundwater samples from fractured-rock aquifers. As part of this effort, monitoring wells from the Stringfellow Superfund site in Jurupa Valley in Riverside County, California, approximately 50 miles east of Los Angeles, were field tested to collect information to assist in the evaluation and application of in-well flow as computed by the analytical model called the Purge Analyzer Tool, which computes in-well groundwater travel times for simple piston transport of inflowing groundwater from open intervals of a monitoring well to the pump intake and can provide insight into optimal purging parameters (duration, rate, and pump position) needed for the collection of representative groundwater samples. Field testing of wells included hydraulic, chemistry, and dye tracer analysis to investigate travel times in wells under pumping conditions. The Purge Analyzer Tool was able to replicate dye velocities (travel times) for one of three wells that had appreciable inflow from the aquifer but not the other two wells, which are screened in low-permeability sediments and rock, where flow was dominated by borehole storage. A set of criteria was established to help assess the ability to collect representative groundwater chemistry from monitoring wells; criteria included understanding the height of the static well water column and relative exchange rate between the aquifer and the well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205140","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Harte, P.T., Perina, T., Becher, K., Levine, H., Rojas-Mickelson, D., Walther, L., and Brown, A., 2021, Evaluation and application of the Purge Analyzer Tool (PAT) to determine in-well flow and purge criteria for sampling monitoring wells at the Stringfellow Superfund site in Jurupa Valley, California, in 2017: U.S. Geological Survey Scientific Investigations Report 2020–5140, 54 p., https://doi.org/10.3133/sir20205140.","productDescription":"Report: ix, 54 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103064","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":384640,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191104","text":"Open-File Report 2019–1104","linkHelpText":"- Instructions for Running the Analytical Code PAT (Purge Analyzer Tool) for Computation of In-Well Time of Travel of Groundwater under Pumping Conditions"},{"id":384639,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CGINH0","text":"USGS data release","linkHelpText":"Data associated with the evaluation of the PAT (Purge Analyzer Tool), Stringfellow Superfund site, Jurupa Valley, California, 2017"},{"id":384641,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5140/coverthb.jpg"},{"id":384642,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5140/sir20205140.pdf","text":"Report","size":"5.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5140"}],"country":"United States","state":"California","city":"Jurupa Valley","otherGeospatial":"Stringfellow Superfund Site","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.47066974639893,\n 34.019585556900935\n ],\n [\n -117.45178699493408,\n 34.019585556900935\n ],\n [\n -117.45178699493408,\n 34.03078943899101\n ],\n [\n -117.47066974639893,\n 34.03078943899101\n ],\n [\n -117.47066974639893,\n 34.019585556900935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Slow-moving landslides are destabilized by accumulated precipitation and consequent soil moisture. Yet, the continuous high-resolution soil-moisture measurements needed to aid the understanding of landslide processes are generally absent in steep terrain. Here, we produce soil-moisture time-series maps for a seasonally active grassland landslide in the northern California coast ranges, USA, using backscattering coefficients from NASA's uninhabited aerial vehicle synthetic aperture radar at 6-m resolution. A physically based radar scattering model is used to retrieve the near-surface (5-cm depth) soil moisture for the landslide. Both forward modeling (backscattering estimation) and the retrieval (soil-moisture validation) show good agreement. The root-mean-square errors (RMSE) for vertical transmit vertical receive (VV) and horizontal transmit horizontal receive (HH) polarizations in forward model comparison are 1.93 dB and 1.88 dB, respectively. The soil-moisture retrieval shows unbiased RMSE of 0.054 m 3 /m 3 . Our successful retrieval benefits from the surface and double-bounce scattering, which is common in grasslands. The retrieved maps show saturated wetness conditions within the active landslide boundaries. We also performed sensitivity tests for incidence angle and found that the retrieval is weakly dependent on the angle, especially while using copolarized HH and VV together. Using the two copolarized inputs, the retrieval is also not sensitive to the change of orientation angles of grass cylinders. The physical model inversion presented here can be generally applied for soil-moisture retrieval in areas with the same vegetation cover types in California.
","language":"English","publisher":"IEEE","doi":"10.1109/JSTARS.2021.3069010","usgsCitation":"Liao, T., Kim, S., Handwerger, A.L., Fielding, E.J., Cosh, M.H., and Schulz, W.H., 2021, High-resolution soil-moisture maps over landslide regions in northern California grassland derived From SAR backscattering coefficients: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 14, p. 4547-4560, https://doi.org/10.1109/JSTARS.2021.3069010.","productDescription":"14 p.","startPage":"4547","endPage":"4560","ipdsId":"IP-126334","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":452942,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/jstars.2021.3069010","text":"Publisher Index Page"},{"id":436434,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NSUMOY","text":"USGS data release","linkHelpText":"Precipitation and soil-moisture data from the Two Towers landslide, Trinity County, California"},{"id":394381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Eel River catchment, Two Towers landslide","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.46761703491211,\n 40.09832911511634\n ],\n [\n -123.45680236816408,\n 40.09832911511634\n ],\n [\n -123.45680236816408,\n 40.10669965638878\n ],\n [\n -123.46761703491211,\n 40.10669965638878\n ],\n [\n -123.46761703491211,\n 40.09832911511634\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Liao, Tien-Hao","contributorId":271098,"corporation":false,"usgs":false,"family":"Liao","given":"Tien-Hao","email":"","affiliations":[{"id":56278,"text":"California Institute of Technology, Division Office Geological and Planetary Sciences","active":true,"usgs":false}],"preferred":false,"id":830812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kim, Seung-bum","contributorId":244954,"corporation":false,"usgs":false,"family":"Kim","given":"Seung-bum","email":"","affiliations":[{"id":27365,"text":"NASA Jet Propulsion Laboratory","active":true,"usgs":false}],"preferred":false,"id":830813,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Handwerger, Alexander L.","contributorId":218095,"corporation":false,"usgs":false,"family":"Handwerger","given":"Alexander","email":"","middleInitial":"L.","affiliations":[{"id":39742,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.","active":true,"usgs":false}],"preferred":false,"id":830814,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fielding, Eric J.","contributorId":218096,"corporation":false,"usgs":false,"family":"Fielding","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":39742,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.","active":true,"usgs":false}],"preferred":false,"id":830815,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cosh, Michael H.","contributorId":146998,"corporation":false,"usgs":false,"family":"Cosh","given":"Michael","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":830816,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":830817,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70219451,"text":"70219451 - 2021 - Physics‐based evaluation of the maximum magnitude of potential earthquakes induced by the Hutubi (China) underground gas storage","interactions":[],"lastModifiedDate":"2021-04-22T17:57:05.216862","indexId":"70219451","displayToPublicDate":"2021-03-25T08:05:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7501,"text":"JGR Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Physics‐based evaluation of the maximum magnitude of potential earthquakes induced by the Hutubi (China) underground gas storage","docAbstract":"The world’s largest underground gas storage facility in Hutubi (HUGS), China, is a unique case where cyclic gas injection‐extraction induced both seismicity and ground deformation. To assess the potential for future induced seismicity, we develop a framework physically based on a well‐constrained hydro‐geomechanical model and on fully coupled poroelastic simulations. We first interpret the spatiotemporal distribution and focal mechanisms of induced earthquakes and use these to estimate the magnitude and location of the largest potential earthquake. The sharp increase in seismicity was controlled by poroelastic loading on secondary southwest‐dipping thrust faults with spatial scales too small to be resolved by 3D seismic surveys. Both operational and local geological factors affect the seismic productivity at the HUGS site, distinguishing it from most cases of seismicity induced by wastewater disposal and hydraulic fracturing. We then conduct slip tendency analyses for major faults imaged by the seismic data, including the largest reservoir‐bounding Hutubi fault hydraulically connected to injection wells. The reactivation potentials of these imaged faults are estimated to be extremely low. Accordingly, future seismicity would most likely occur on failure‐prone secondary faults in regions with positive stress perturbation due to poroelastic loading. The maximum magnitude likely depends on the spatial scales of the secondary faults. As the occurrence of detected earthquakes is spatially and temporally consistent with the simulated evolution of Coulomb stress perturbation, the location of the largest potential earthquake probably depends on the sizes of the poroelastic stressing regions.
Invasive carp are expanding throughout the upper Mississippi River basin and are of great concern due to their potential economic and ecological impacts. Identification of spawning locations provides critical information on recruitment sources to evaluate potential management strategies. Our objective was to create and validate a spawning habitat suitability model of the Des Moines River, Iowa, during low-, average-, and high-water-level conditions. Backwater availability, abundance of hardpoints (structures that create turbulence), river gradient and sinuosity, water temperature, and continuously free-flowing river lengths were used as model parameters. The model was compared to back-calculated spawning locations from invasive carp eggs collected in 2014–2015. Turbulent hardpoints, river sinuosity, and gradient were not significant predictors of invasive carp spawning locations, and backwater availability in the 25 river kilometers downstream of each reach was inversely correlated with invasive carp spawning locations. Invasive carp eggs were not caught in 2014 despite optimal spawning conditions, revealing that spawning may have high interannual variation. This study suggests that predicting invasive carp reproduction may require variables in addition to those currently proposed.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10551","usgsCitation":"Camacho, C., Sullivan, C., Weber, M., and Pierce, C., 2021, Suitability of an upper Mississippi River tributary for invasive carp reproduction: North American Journal of Fisheries Management, v. 43, no. 1, p. 12-24, https://doi.org/10.1002/nafm.10551.","productDescription":"13 p.","startPage":"12","endPage":"24","ipdsId":"IP-081177","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480927,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa","otherGeospatial":"Des Moines 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\"}}]}","volume":"43","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Camacho, Carlos A.","contributorId":348841,"corporation":false,"usgs":false,"family":"Camacho","given":"Carlos A.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":923827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Christopher J.","contributorId":348842,"corporation":false,"usgs":false,"family":"Sullivan","given":"Christopher J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":923828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weber, Michael J.","contributorId":348844,"corporation":false,"usgs":false,"family":"Weber","given":"Michael J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":923829,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pierce, Clay 0000-0001-5088-5431 cpierce@usgs.gov","orcid":"https://orcid.org/0000-0001-5088-5431","contributorId":150492,"corporation":false,"usgs":true,"family":"Pierce","given":"Clay","email":"cpierce@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923826,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219098,"text":"sir20205120 - 2021 - Assessment of water quality and discharge in the Herring River, Wellfleet, Massachusetts, November 2015 to September 2017","interactions":[],"lastModifiedDate":"2021-03-24T22:26:41.479816","indexId":"sir20205120","displayToPublicDate":"2021-03-24T15:35:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5120","displayTitle":"Assessment of Water Quality and Discharge in the Herring River, Wellfleet, Massachusetts, November 2015 to September 2017","title":"Assessment of water quality and discharge in the Herring River, Wellfleet, Massachusetts, November 2015 to September 2017","docAbstract":"The U.S. Geological Survey, Cape Cod National Seashore of the National Park Service, and Friends of Herring River cooperated from 2015 to 2017 to assess nutrient concentrations and fluxes across the ocean-estuary boundary at a dike on the Herring River in Wellfleet, Massachusetts. The purpose of this assessment was to characterize environmental conditions prior to a future removal of the dike, which has restricted saltwater inputs into the Herring River watershed for more than 100 years. Water temperature, dissolved oxygen, pH, and specific conductance were monitored continuously, and flow-weighted composite samples were collected approximately twice per month at the ocean-estuary boundary. Bidirectional discharge was computed for the U.S. Geological Survey Herring River at Chequessett Neck Road at Wellfleet, Massachusetts, streamgage (011058798) by using a stage-area rating and index-velocity ratings developed with acoustic Doppler current profile measurements made upstream and downstream from the dike. LOADEST regression modeling software was used to estimate nutrient fluxes (loads) from composite, paired nutrient concentration and discharge data in conjunction with continuous discharge data. Temperature, dissolved oxygen, pH, and specific conductance were also monitored continuously on two tributaries to the Herring River, Pole Dike Creek and Bound Brook, from late-May 2016 to mid-June 2017. Composite or discrete water samples were collected from the tributaries approximately twice per month in most months from late-May 2016 to mid-June 2017 and analyzed for total nitrogen, total phosphorus, and dissolved organic carbon.
Flow-weighted concentrations of ammonium, nitrate, and total nitrogen on the Herring River at the dike on the ebb tide generally varied between 0.01 and 0.1, 0.003 and 0.03, and 0.3 and 0.7 milligram per liter as nitrogen, respectively. Flow-weighted concentrations of orthophosphate, total dissolved phosphorus, and total phosphorus generally varied between 0.002 and 0.02, 0.003 and 0.06, and 0.03 and 0.1 milligram per liter as phosphorus, respectively, on the ebb tide. Flow-weighted concentrations of silicate and dissolved organic carbon on the ebb tide generally varied between 0.08 and 3.0 milligrams per liter of silica (silicon dioxide), and 1.7 and 5.6 milligrams per liter of carbon, respectively. Ebb tide concentrations of nitrate were highest in winter and lowest in summer. By contrast, ebb tide concentrations of phosphorus species were highest in late summer and early fall and lowest in winter. Silica and dissolved organic carbon did not exhibit systematic variation in seasonal concentrations. There was uncertainty in estimates of nutrient fluxes, but the LOADEST-estimated fluxes indicated that annual (and in almost all cases seasonal) exports (ebb tides) exceeded inputs (flood tides). Ebb tide concentrations of ammonium, nitrate, total nitrogen, and silica were positively correlated with antecedent cumulative 7-day precipitation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205120","collaboration":"Prepared in cooperation with the National Park Service and Friends of Herring River","usgsCitation":"Huntington, T.G., Spaetzel, A.B., Colman, J.A., Kroeger, K.D., and Bradley, R.T., 2021, Assessment of water quality and discharge in the Herring River, Wellfleet, Massachusetts, November 2015 to September 2017: U.S. Geological Survey Scientific Investigations Report 2020–5120, 59 p., https://doi.org/10.3133/sir20205120.","productDescription":"Report: x, 59 p.; Data Release","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106718","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":384601,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5120/coverthb.jpg"},{"id":384603,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BKW4BX","text":"USGS data release","linkHelpText":"Tidal daily discharge and quality assurance data supporting an assessment of water quality and discharge in the Herring River, Wellfleet, Massachusetts, November 2015–September 2017"},{"id":384602,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5120/sir20205120.pdf","text":"Report","size":"3.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5120"}],"country":"United States","state":"Massachusetts","city":"Wellfleet","otherGeospatial":"Herring River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.07801055908203,\n 41.93318868195924\n ],\n [\n -69.99870300292969,\n 41.93318868195924\n ],\n [\n -69.99870300292969,\n 41.98833256890643\n ],\n [\n -70.07801055908203,\n 41.98833256890643\n ],\n [\n -70.07801055908203,\n 41.93318868195924\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Soil and groundwater at the Badger Army Ammunition Plant (BAAP), Sauk County, Wisconsin, were affected by several contaminants as a result of production and waste disposal practices common during its operation from 1942 to 1975. Three distinct plumes of contaminated groundwater originate on BAAP property and extend off-site, as identified by previous studies. Routine sampling of groundwater quality from a network of monitoring wells and off-site private wells has been performed since 1990, although the number of wells monitored and the monitoring frequency have varied as the approved monitoring plan was modified. During the period of monitoring from 1990 to 2018, numerous site investigations and remedial actions were conducted to address the sources of contamination, contaminated soils, and groundwater. Concentrations of contaminants reportedly decreased between 2000 and 2012 within all three plumes. Five or six contaminants of concern (COCs) were identified for each of the three plumes. An independent assessment of the contaminant plumes and of the monitoring network was conducted using groundwater-quality data collected from more than 600 wells between 2000 and 2018.
In a study conducted by the U.S. Geological Survey (USGS), in cooperation with the Army Environmental Command, a consistent data aggregation and interpolation scheme was applied to derive the likely maximum groundwater plume extents in four 3-year time periods between 2000 and 2018. The plume extent was defined by the Enforcement Standard for each COC and represents the maximum concentration observed in each 3-year time period. The plume boundary analysis shows that the spatial extent of groundwater contamination decreased for most COCs during the study period. Some plume boundaries are not well delineated by the existing monitoring network, particularly the downgradient edge of the Propellant Burning Ground plume. Maps identify the plume boundary in each time period, the sampling well network used to delineate the plume, and wells that were sampled in the 2010–12 period but not sampled in the 2015–18 period.
A series of statistical analyses using the Monitoring and Remediation Optimization System, version 3.0, program were applied to the available COC concentration data for two distinct periods, 2000 to 2012 and 2013 to 2018, with the break between periods coinciding with changes to the monitoring network in 2013. Trends in the concentration of COCs in individual wells varied, although generally more wells had decreasing than had increasing concentrations for most COCs in both time periods. The exceptions were ethyl ether in the 2004–12 period and 2,6-dinitrotoluene in the 2013–18 period, for which more wells had an increasing trend. Spatial moment analysis of concentration data from the well network was used to assess the stability of each plume for the COCs. During the 2000–12 period, most of the contaminant plumes for which data were sufficient to complete the analysis were either decreasing or stable in mass and size. The exceptions were carbon tetrachloride (associated solely with the Propellant Burning Ground plume) and 2,4-dinitrotoluene and 2,6-dinitrotoluene (in the Deterrent Burning Ground plume), which showed an increasing trend in mass. No COCs showed an increasing trend in plume mass in the 2013–18 period. Some wells with increasing trends in concentration or with concentrations greater than the enforcement standard are near the tail of a plume, where increased monitoring may be of value to better define future plume boundaries. A spatial optimization analysis covering the 2013–18 period identified six wells that provided information redundant to that from other wells. A temporal optimization analysis identified optimal sampling frequencies for 125 wells. Remedial actions directed at the Propellant Burning Ground plume coincided with a general decrease in plume mass and size, although in specific areas and depths, the plume size for specific contaminants may still be increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205106","collaboration":"Prepared in cooperation with the Army Environmental Command","usgsCitation":"Pajerowski, M., Goodling, P., and Metes, M., 2021, Assessment of contaminant trends in plumes and wells and monitoring network optimization at the Badger Army Ammunition Plant, Sauk County, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2020–5106, 80 p., https://doi.org/10.3133/sir20205106.","productDescription":"Report: x, 80 p.; Data Release; 16 Plates","numberOfPages":"80","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118955","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":384411,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2020/5106/sir20205106_plates.pdf","text":"Plates 1 through 16","size":"189 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":384401,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5106/coverthb.jpg"},{"id":384402,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5106/sir20205106.pdf","text":"Report","size":"10.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5106"},{"id":384403,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97UKYNR","text":"USGS data release","linkHelpText":"Groundwater quality and plume boundaries for select contaminants of concern at Badger Army Ammunition Plant, Wisconsin (2000–2018)"}],"country":"United States","state":"Wisconsin","county":"Sauk County","otherGeospatial":"Badger Army Ammunition Plant","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.77375030517578,\n 43.30694264971061\n ],\n [\n -89.67041015625,\n 43.30694264971061\n ],\n [\n -89.67041015625,\n 43.420634784134876\n ],\n [\n -89.77375030517578,\n 43.420634784134876\n ],\n [\n -89.77375030517578,\n 43.30694264971061\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228
The Everglades wetland was once a river of grass, with water flowing slowly through the sawgrass, southward across the landscape. As developers took hold of south Florida, water was sent away from the heart of the Everglades through canals and levees to protect the former wetland for residential and agricultural development. In the 1990s, planning began to restore the Everglades in what is the largest hydrologic restoration undertaking in the world. With billions of taxpayer dollars at stake, restoration planners benefit from forecasting tools to inform restoration planning. To meet this need, scientists developed predictive ecological models and other decision support tools tailored to this dynamic ecosystem as well as to the needs of restoration planning teams. Predictive modeling has been able to take advantage of well-understood relationships between species of interest and hydrologic dynamics in the Everglades. Recent modeling advances include multi-species approaches that consider interactions among species as well as explicit consideration of trade-offs among species from potential water management actions. Scientists are also starting to look at ecosystem-wide vulnerabilities with explicit consideration of future change such as sea level rise. Modeling tools and approaches continue to be refined to meet decision making needs for Everglades restoration. However, more work is needed to consider additional complexities of this dynamic wetland as well as to consider the broader socio-environmental system.
Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13061210","usgsCitation":"Benedict, T.D., Brown, J.F., Boyte, S., Howard, D., Fuchs, B., Wardlow, B.D., Tadesse, T., and Evenson, K., 2021, Exploring VIIRS continuity with MODIS in an expedited capability for monitoring drought-related vegetation conditions: Remote Sensing, v. 13, no. 6, 1210, 17 p., https://doi.org/10.3390/rs13061210.","productDescription":"1210, 17 p.","ipdsId":"IP-126651","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":452960,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13061210","text":"Publisher Index Page"},{"id":384696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 37.68382032669382\n ],\n [\n -94.833984375,\n 37.68382032669382\n ],\n [\n -94.833984375,\n 39.977120098439634\n ],\n [\n -98.61328125,\n 39.977120098439634\n ],\n [\n -98.61328125,\n 37.68382032669382\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Benedict, Trenton D 0000-0001-8672-2204","orcid":"https://orcid.org/0000-0001-8672-2204","contributorId":256662,"corporation":false,"usgs":false,"family":"Benedict","given":"Trenton","email":"","middleInitial":"D","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":812983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Jesslyn F. 0000-0002-9976-1998 jfbrown@usgs.gov","orcid":"https://orcid.org/0000-0002-9976-1998","contributorId":176609,"corporation":false,"usgs":true,"family":"Brown","given":"Jesslyn","email":"jfbrown@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":812984,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":812985,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howard, Daniel 0000-0002-7563-7538","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":256667,"corporation":false,"usgs":false,"family":"Howard","given":"Daniel","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":812986,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuchs, Brian","contributorId":192359,"corporation":false,"usgs":false,"family":"Fuchs","given":"Brian","email":"","affiliations":[],"preferred":false,"id":812987,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wardlow, Brian D. 0000-0002-4767-581X","orcid":"https://orcid.org/0000-0002-4767-581X","contributorId":191403,"corporation":false,"usgs":false,"family":"Wardlow","given":"Brian","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":812988,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tadesse, Tsegaye 0000-0002-4102-1137","orcid":"https://orcid.org/0000-0002-4102-1137","contributorId":147617,"corporation":false,"usgs":false,"family":"Tadesse","given":"Tsegaye","email":"","affiliations":[],"preferred":false,"id":812989,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Evenson, Kirk","contributorId":256674,"corporation":false,"usgs":false,"family":"Evenson","given":"Kirk","email":"","affiliations":[{"id":51826,"text":"KBR, Inc. Contractor to the USGS Earth Resources Observation & Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":812990,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70223101,"text":"70223101 - 2021 - Assessment of sea lamprey (Petromyzon marinus) diet using DNA metabarcoding of feces","interactions":[],"lastModifiedDate":"2021-08-11T14:40:29.799032","indexId":"70223101","displayToPublicDate":"2021-03-23T09:32:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Assessment of sea lamprey (Petromyzon marinus) diet using DNA metabarcoding of feces","title":"Assessment of sea lamprey (Petromyzon marinus) diet using DNA metabarcoding of feces","docAbstract":"Sea lamprey (Petromyzon marinus) are invasive in the Laurentian Great Lakes, parasitize large-bodied fishes, and therefore are the focus of an international control program. However, damage caused by sea lamprey to modern day fish stocks remains uncertain because diet analysis of juvenile sea lamprey has been challenging; they feed on blood and are difficult to randomly sample in the lakes. Here, both challenges were addressed by showing that DNA metabarcoding of fecal material can be used to identify the diet of actively feeding juvenile sea lamprey, and can also be used to determine what non-feeding adult sea lamprey captured in streams fed on while parasitizing fish. Fecal samples from juvenile sea lamprey that were feeding on lake trout in northern Lake Huron overwhelmingly contained lake trout (Salvelinus namaycush) DNA (90%), while smaller percentages contained lake whitefish (Coregonus clupeaformis; 5%) and longnose sucker (Catostomus catostomus; 5%) DNA. Fecal samples from adult sea lamprey captured from a tributary to northern Lake Huron overwhelmingly contained longnose and white sucker DNA (Catostomus spp.; 80%), while a smaller percentage contained lake trout DNA (10%). Diet composition of adult sea lamprey sampled in the tributary (Black Mallard Creek) was more diverse than juvenile diet composition. DNA metabarcoding suggests that Catostomus spp. may be an important host fish in northern Lake Huron for sea lamprey prior to spawning. Future research could investigate how diet varies across years and lakes and the prevalence and sources of DNA contamination. Application of DNA metabarcoding for diet assessment may be practical for identifying populations of invasive sea lamprey that feed on highly valued fishes and help guide restoration of lampreys worldwide.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.107605","usgsCitation":"Johnson, N.S., Lewandoski, S.A., and Merkes, C.M., 2021, Assessment of sea lamprey (Petromyzon marinus) diet using DNA metabarcoding of feces: Ecological Indicators, v. 125, 107605, 9 p., https://doi.org/10.1016/j.ecolind.2021.107605.","productDescription":"107605, 9 p.","ipdsId":"IP-127616","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":452962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.107605","text":"Publisher Index Page"},{"id":387856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Hammond Bay, Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.99459838867188,\n 45.49431506693786\n ],\n [\n -83.9520263671875,\n 45.53232688809725\n ],\n [\n -83.97880554199219,\n 45.58761466314763\n ],\n [\n -84.06257629394531,\n 45.6029894122523\n ],\n [\n -84.11819458007811,\n 45.58569252333191\n ],\n [\n -84.12574768066406,\n 45.558294879426235\n ],\n [\n -84.11338806152344,\n 45.511640093571096\n ],\n [\n -84.07424926757812,\n 45.49238973487207\n ],\n [\n -84.02549743652344,\n 45.50057194157223\n ],\n [\n -83.99459838867188,\n 45.49431506693786\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":597,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas","email":"njohnson@usgs.gov","middleInitial":"S.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":820952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lewandoski, Sean A.","contributorId":221007,"corporation":false,"usgs":false,"family":"Lewandoski","given":"Sean","email":"","middleInitial":"A.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":820953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820954,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222609,"text":"70222609 - 2021 - Geological constraints on the mechanisms of slow earthquakes","interactions":[],"lastModifiedDate":"2021-08-09T13:57:48.415744","indexId":"70222609","displayToPublicDate":"2021-03-23T08:54:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9136,"text":"Nature Reviews Earth and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Geological constraints on the mechanisms of slow earthquakes","docAbstract":"The recognition of slow earthquakes in geodetic and seismological data has transformed the understanding of how plate motions are accommodated at major plate boundaries. Slow earthquakes, which slip more slowly than regular earthquakes but faster than plate motion velocities, occur in a range of tectonic and metamorphic settings. They exhibit spatiotemporal associations with large seismic events that indicate a causal relation between modes of slip at different slip rates. Defining the physical controls on slow earthquakes is, therefore, critical for understanding fault and shear zone mechanics. In this Review, we synthesize geological observations of a suite of ancient structures that were active in tectonic settings comparable to where slow earthquakes are observed today. At inferred slow earthquake regions, a range of grain-scale deformation mechanisms accommodated slip at low effective stresses. Material heterogeneity and the geometric complexity of structures that formed at different inferred strain rates are common to faults and shear zones in multiple tectonic environments, and might represent key limiting factors of slow earthquake slip rates. Further geological work is needed to resolve how the spectrum of slow earthquake slip rates can arise from different grain-scale deformation mechanisms and whether there is one universal rate-limiting mechanism that defines slow earthquake slip.
","language":"English","publisher":"Nature Publications","doi":"10.1038/s43017-021-00148-w","usgsCitation":"Kirkpatrick, J.D., Fagereng, A., and Shelly, D.R., 2021, Geological constraints on the mechanisms of slow earthquakes: Nature Reviews Earth and Environment, v. 2, p. 285-301, https://doi.org/10.1038/s43017-021-00148-w.","productDescription":"17 p.","startPage":"285","endPage":"301","ipdsId":"IP-124320","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":467251,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://orca.cardiff.ac.uk/id/eprint/140270/","text":"External Repository"},{"id":387780,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Kirkpatrick, James D.","contributorId":202603,"corporation":false,"usgs":false,"family":"Kirkpatrick","given":"James","email":"","middleInitial":"D.","affiliations":[{"id":6646,"text":"McGill University","active":true,"usgs":false}],"preferred":false,"id":820736,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fagereng, Ake","contributorId":261903,"corporation":false,"usgs":false,"family":"Fagereng","given":"Ake","email":"","affiliations":[{"id":53076,"text":"School of Earth & Ocean Sciences, Cardiff University, Cardiff, CF10 3AT, UK","active":true,"usgs":false}],"preferred":false,"id":820737,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":820803,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70218995,"text":"pp1842DD - 2021 - The effects of management practices on grassland birds—Lark Sparrow (Chondestes grammacus)","interactions":[{"subject":{"id":70218995,"text":"pp1842DD - 2021 - The effects of management practices on grassland birds—Lark Sparrow (Chondestes grammacus)","indexId":"pp1842DD","publicationYear":"2021","noYear":false,"chapter":"DD","displayTitle":"The Effects of Management Practices on Grassland Birds—Lark Sparrow (Chondestes grammacus)","title":"The effects of management practices on grassland birds—Lark Sparrow (Chondestes grammacus)"},"predicate":"IS_PART_OF","object":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"id":1}],"isPartOf":{"id":70203022,"text":"pp1842 - 2019 - The effects of management practices on grassland birds","indexId":"pp1842","publicationYear":"2019","noYear":false,"title":"The effects of management practices on grassland birds"},"lastModifiedDate":"2023-12-20T20:56:32.615583","indexId":"pp1842DD","displayToPublicDate":"2021-03-23T08:45:33","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1842","chapter":"DD","displayTitle":"The Effects of Management Practices on Grassland Birds—Lark Sparrow (Chondestes grammacus)","title":"The effects of management practices on grassland birds—Lark Sparrow (Chondestes grammacus)","docAbstract":"Keys to Lark Sparrow (Chondestes grammacus) management include providing open grasslands with sparse-to-moderate herbaceous and litter cover and a woody component and allowing occasional burning or moderate grazing. Lark Sparrows have been reported to use habitats with 10–63 centimeters (cm) average vegetation height, 10–54 percent grass cover, 9–25 percent forb cover, 4–18 percent shrub cover, 16–38 percent bare ground, 12–45 percent litter cover, and less than or equal to 1 cm litter depth.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1842DD","usgsCitation":"Shaffer, J.A., Igl, L.D., Johnson, D.H., Sondreal, M.L., Goldade, C.M., Parkin, B.D., and Euliss, B.R., 2021, The effects of management practices on grassland birds—Lark Sparrow (Chondestes grammacus), chap. DD of Johnson, D.H., Igl, L.D., Shaffer, J.A., and DeLong, J.P., eds., The effects of management practices on grassland birds: U.S. Geological Survey Professional Paper 1842, 16 p., https://doi.org/10.3133/pp1842DD.","productDescription":"iv, 21 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-095209","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":384458,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1842/dd/coverthb.jpg"},{"id":384459,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1842/dd/pp1842dd.pdf","text":"Report","size":"2.09 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1842–DD"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
In the humid, temperate Delaware River Basin (DRB) where water availability is generally reliable, summer low flows can cause competition between various human and ecological water uses. As temperatures continue to rise, population increases and development expands, it is critical to understand historical low flow variability to anticipate and plan for future flows. Using a sample of 325 U.S. Geological Survey gages, we evaluated spatial patterns in several low flow metrics, the biophysical and climatic drivers of these metrics, and trends in low flows for two periods: 1950-2018 and 1980-2018. We calculated the annual 7-day low flow and date, low flow deficit as the departure below a long-term daily flow threshold and the number of discrete low flow periods below this threshold. We also aggregated several climate metrics to watershed scale and used existing watershed properties quantifying land cover, topography, soils, geology, and human activity. Random forest models were used to assess the hierarchy of variable importance in explaining mean-annual low flow variability for each low flow metric using all gages. We find muted regional patterns in mean-annual low flow and low flow variability, likely due to the myriad of anthropogenic, landscape, and flow modifications that obscure flow regimes from their natural characteristics. In contrast, individual years show markedly different spatial patterns in low flow magnitude and severity. Coincident with increases in precipitation, 7-day low flows have generally increased and low flow deficits decreased for both 1950-2018 and 1980-2018 periods. However, 7-day low flows have decreased in the Coastal Plain physiographic province where water use and impervious area have increased in recent decades, highlighting the effects of land and water management on low flows. With continued change expected in the DRB, additional research needs are highlighted to enable estimation of future low flows and to plan for periods of prolonged low flow.
A multi-component geochemical dataset was collected from groundwater and surface-water bodies associated with the urban Fountain Creek alluvial aquifer, Colorado, USA, to facilitate analysis of recharge sources, geochemical interactions, and groundwater-residence times. Results indicate that groundwater can be separated into three distinct geochemical zones based on location within the flow system and proximity to surface water, and these zones can be used to infer sources of recharge and groundwater movement through the aquifer. Rare-earth-element concentrations and detections of wastewater-indicator compounds indicate the presence of effluent from wastewater-treatment plants in both groundwater and surface water. Effluent presence in groundwater indicates that streams in the area lose to groundwater in some seasons and are a source of focused groundwater recharge. Distributions of pharmaceuticals and wastewater-indicator compounds also inform an understanding of groundwater–surface-water interactions. Noble-gas isotopes corroborate rare-earth-element data in indicating geochemical evolution within the aquifer from recharge area to discharge area and qualitatively indicate variable groundwater-residence times and mixing with pre-modern groundwater. Quantitative groundwater-residence times calculated from 3H/3He, SF6, and lumped-parameter modeling generally are less than 20 years, but the presence of mixing with older groundwater of an unknown age is also indicated at selected locations. Future investigations would benefit by including groundwater-age tracers suited to quantification of mixing for both young (years to decades) and old (centuries and millennia) groundwater. This multi-faceted analysis facilitated development of a conceptual model for the investigated groundwater-flow system and illustrates the application of an encompassing suite of analytes in exploring hydrologic and geochemical interactions in complex systems.
","language":"English","publisher":"MDPI","doi":"10.3390/w13060871","usgsCitation":"Newman, C.P., Paschke, S.S., and Keith, G.L., 2021, Natural and anthropogenic geochemical tracers to investigate residence times and groundwater–surface-water interactions in an urban alluvial aquifer: Water, v. 13, no. 6, 30 p., https://doi.org/10.3390/w13060871.","productDescription":"30 p.","ipdsId":"IP-118155","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":452974,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w13060871","text":"Publisher Index Page"},{"id":436443,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99SPQM2","text":"USGS data release","linkHelpText":"Environmental-tracer modeling to support hydrogeochemical evaluation of the Fountain Creek Alluvial Aquifer, El Paso County, Colorado, 2018-2019"},{"id":384712,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","city":"Colorado Springs","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.2490234375,\n 38.61687046392973\n ],\n [\n -104.1888427734375,\n 38.61687046392973\n ],\n [\n -104.1888427734375,\n 39.16839998800286\n ],\n [\n -105.2490234375,\n 39.16839998800286\n ],\n [\n -105.2490234375,\n 38.61687046392973\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Newman, Connor P. 0000-0002-6978-3440","orcid":"https://orcid.org/0000-0002-6978-3440","contributorId":222596,"corporation":false,"usgs":true,"family":"Newman","given":"Connor","email":"","middleInitial":"P.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813075,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paschke, Suzanne S. 0000-0002-3471-4242 spaschke@usgs.gov","orcid":"https://orcid.org/0000-0002-3471-4242","contributorId":1347,"corporation":false,"usgs":true,"family":"Paschke","given":"Suzanne","email":"spaschke@usgs.gov","middleInitial":"S.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813076,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keith, Gabrielle L. 0000-0002-2304-8504 gkeith@usgs.gov","orcid":"https://orcid.org/0000-0002-2304-8504","contributorId":256699,"corporation":false,"usgs":true,"family":"Keith","given":"Gabrielle","email":"gkeith@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813077,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259605,"text":"70259605 - 2021 - Assessment of a claimed ultra-low frequency electromagnetic (ULFEM) earthquake precursor","interactions":[],"lastModifiedDate":"2024-10-17T12:04:01.889156","indexId":"70259605","displayToPublicDate":"2021-03-23T07:01:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of a claimed ultra-low frequency electromagnetic (ULFEM) earthquake precursor","docAbstract":"The rate of occurrence of anomalous ultra-low frequency electromagnetic (ULFEM) pulses has been claimed to have increased days to weeks prior to the M5.4 2007 and M4.0 2010 Alum Rock earthquakes. We re-examine the previously reported ultra-low frequency (ULF: 0.01–10 Hz) magnetic data recorded at a QuakeFinder site located 9 km from the earthquake hypocentre, and compare to data from a nearby Stanford-USGS site located 42 km from the hypocentre, to analyse the characteristics of the pulses and assess their origin. Using pulse definitions and pulse-counting algorithms analogous to those previously reported, we corroborate the increase in pulse counts before the 2007 Alum Rock earthquake at the QuakeFinder station, but we note that the number of pulses depends on chosen temporal and amplitude detection thresholds. These thresholds are arbitrary because we lack a clear physical model or basis for their selection. We do not see the same increase in pulse counts before the 2010 Alum Rock earthquake at the QuakeFinder or Stanford-USGS stations. In addition, the majority of pulses in the QuakeFinder data and Stanford-USGS data do not match temporally, indicating the pulses lack a common origin and are not from lightning or solar-driven ionospheric/magnetospheric disturbances. Our assessment of the temporal distribution of pulse counts shows pulse counts increase during peak human activity hours, suggesting these pulses result from local cultural noise and are not tectonic in origin. The many unknowns about the character and even existence of precursory earthquake pulses means that standard numerical and statistical tests cannot easily be applied. Yet here we show that exhaustive investigation of many different aspects of ULFEM signals can be used to properly characterize their origin.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggab530","usgsCitation":"Wang, C., Christman, L., Klemperer, S., Glen, J.M., McPhee, D., and Bin, C., 2021, Assessment of a claimed ultra-low frequency electromagnetic (ULFEM) earthquake precursor: Geophysical Journal International, v. 229, no. 3, p. 2081-2095, https://doi.org/10.1093/gji/ggab530.","productDescription":"15 p.","startPage":"2081","endPage":"2095","ipdsId":"IP-136197","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":467252,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggab530","text":"Publisher Index Page"},{"id":462937,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.74855580223482,\n 37.923464830974424\n ],\n [\n -122.74855580223482,\n 37.22689363596939\n ],\n [\n -121.73781361473472,\n 37.22689363596939\n ],\n [\n -121.73781361473472,\n 37.923464830974424\n ],\n [\n -122.74855580223482,\n 37.923464830974424\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"229","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Can","contributorId":345181,"corporation":false,"usgs":false,"family":"Wang","given":"Can","email":"","affiliations":[{"id":82515,"text":"Institute of Geophysics, China Earthquake Administration, Beijing 100081, P.R. 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After several years of many applications and uses of a previous version, CA-BCM, published in 2014, the BCMv8 was refined to improve the accuracy of the water-balance components, particularly the recharge estimate, which is the most difficult to accurately assess. The improvement of the various water-balance components targeted the actual evapotranspiration component, which, in turn, reduced the uncertainty of the recharge estimate. The improvement of this component was enabled by the availability of a national, gridded actual-evapotranspiration product from the U.S. Geological Survey that was unique in its scope to combine remotely sensed spatial variability and ground-based long-term water-balance constraints. This dataset provided the ability to assess monthly actual evapotranspiration for 62 vegetation types and to perform regional calibration in watersheds throughout California with the objective of closing the water balance using improved estimates for each component. The refinements, including vegetation-specific evapotranspiration, enabled the development of applications that could explore various aspects of landscape disturbance, such as wildfire, forest management, or urbanization. The improvements to BCMv8 also provided the ability to assess long-term sustainability of water resources under a variety of management applications or future climate projections.
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\"}}]}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Climate change has and is projected to continue to alter historical regimes of temperature, precipitation, and hydrology. To assess the vulnerability of climate change from a land management perspective and spatially identify where the most extreme changes are anticipated to occur, we worked in collaboration with land managers to develop a climate change vulnerability map for the midwestern United States with a focus on riparian systems. The map is intended for use by regional administrators to help them work across various program areas (e.g. fisheries, endangered species) to prioritize locations needing support for adaptation planning. The tool can also be utilized locally by managers to better understand the effects that projected climate scenarios have on the hydrology of management units as they develop adaptation strategies. The vulnerability map is watershed-based (360 watershed units within the region) and combines 15 climate change indicators that were selected by U.S. Fish and Wildlife Service natural resource managers based upon known and anticipated effects to species and habitats. The projected change in each of these indicators from the historical period (1986–2005) to the future period (2040–2059) was aggregated into a composite score for each watershed. Landscape-scale metrics reflective of a watershed’s adaptive capacity were combined with the climate change indicators to produce a vulnerability score. We found sub-regional variation in vulnerability to climate change with the greatest vulnerability in Iowa, central Illinois, and northwest Ohio. Greater vulnerability was seen in the higher greenhouse gas concentration scenario, Representative Concentration Pathway (RCP) 8.5 compared to the lower greenhouse gas concentration scenario RCP 4.5, when looking at the mean of the five downscaled climate models used in this study. By quantifying and mapping climate change vulnerability, natural resource managers can better understand the degree of vulnerability for individual watersheds and identify areas of prioritization in regional and local planning efforts.
Elevation is a major driver of plant ecology and sediment dynamics in tidal wetlands, so accurate and precise spatial data are essential for assessing wetland vulnerability to sea-level rise and making forecasts. We performed survey-grade elevation and vegetation surveys of the Global Change Research Wetland, a brackish microtidal wetland in the Chesapeake Bay estuary, Maryland (USA), to both intercompare unbiased digital elevation model (DEM) creation techniques and to describe niche partitioning of several common tidal wetland plant species. We identified a tradeoff between scalability and performance in creating unbiased DEMs, with more data-intensive methods such as kriging performing better than 3 more scalable methods involving post-processing of light detection and ranging (LiDAR)-based DEMs. The LiDAR Elevation Correction with Normalized Difference Vegetation Index (LEAN) method provided a compromise between scalability and performance, although it underpredicted variability in elevation. In areas where native plants dominated, the sedge Schoenoplectus americanus occupied more frequently flooded areas (median: 0.22, 95% range: 0.09 to 0.31 m relative to North America Vertical Datum of 1988 [NAVD88]) and the grass Spartina patens, less frequently flooded (0.27, 0.1 to 0.35 m NAVD88). Non-native Phragmites australis dominated at lower elevations more than the native graminoids, but had a wide flooding tolerance, encompassing both their ranges (0.19, -0.05 to 0.36 m NAVD88). The native shrub Iva frutescens also dominated at lower elevations (0.20, 0.04 to 0.30 m NAVD88), despite being previously described as a high marsh species. These analyses not only provide valuable context for the temporally rich but spatially restricted data collected at a single well-studied site, but also provide broad insight into mapping techniques and species zonation.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13683","usgsCitation":"Holmquist, J., Schile-Beers, L., Buffington, K., Lu, M., Mozdzer, T.J., Riera, J., Weller, D.E., Williams, M., and Megonigal, J., 2021, Scalability and performance tradeoffs in quantifying relationships between elevation and tidal wetland plant communities: Marine Progress Series, v. 666, p. 57-72, https://doi.org/10.3354/meps13683.","productDescription":"16 p.","startPage":"57","endPage":"72","ipdsId":"IP-127317","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":452993,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/meps13683","text":"Publisher Index Page"},{"id":414537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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College","active":true,"usgs":false}],"preferred":false,"id":867020,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Riera, Jefferson","contributorId":303282,"corporation":false,"usgs":false,"family":"Riera","given":"Jefferson","email":"","affiliations":[{"id":36717,"text":"Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":867021,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Weller, Donald E.","contributorId":199780,"corporation":false,"usgs":false,"family":"Weller","given":"Donald","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":867022,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Williams, Meghan","contributorId":303283,"corporation":false,"usgs":false,"family":"Williams","given":"Meghan","email":"","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":867023,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Megonigal, J Patrick","contributorId":228820,"corporation":false,"usgs":false,"family":"Megonigal","given":"J Patrick","affiliations":[{"id":41515,"text":"Smithsonian Env Res Ctr","active":true,"usgs":false}],"preferred":false,"id":867024,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70219519,"text":"70219519 - 2021 - A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems","interactions":[],"lastModifiedDate":"2021-06-30T18:00:05.022669","indexId":"70219519","displayToPublicDate":"2021-03-21T08:48:19","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems","docAbstract":"Groundwater models have evolved to encompass more aspects of the water cycle, but the incorporation of realistic boundary conditions representing surface water remains time-consuming and error-prone. We present two Python packages that robustly automate this process using readily available hydrography data as the primary input. SFRmaker creates input for the MODFLOW SFR package, while Linesink-maker creates linesink string input for the GFLOW analytic element program. These programs can reduce weeks or even months of manual effort to a few minutes of execution time, and carry the added advantages of reduced potential for error, improved reproducibility and facilitation of step-wise modeling through reduced dependency on a particular conceptual model or discretization. Two real-world examples at the county to multi-state scales are presented.
The U.S. Geological Survey, in cooperation with the District Department of Energy & Environment, Water Quality Division, is investigating the hydrogeology of the tidal Anacostia River watershed within Washington, D.C., with the goal of improving understanding of the groundwater-flow system and the interaction of groundwater and surface water in the watershed. To help meet this goal, a three-dimensional steady-state groundwater-flow model for the Anacostia River and surrounding watersheds in Washington, D.C., Maryland, and Virginia was constructed. The goal of the modeling study was to quantify the rate and pattern of groundwater flow to the tidal Anacostia River. The model domain includes weathered and unweathered rocks of the Piedmont Physiographic Province and the southeast-dipping sediments of the Atlantic Coastal Plain Physiographic Province. The model includes processes of recharge, evapotranspiration, withdrawals from wells, and base flow to streams, rivers, and tidal waters. Final model calibration was achieved by using the objective parameter estimation and sensitivity analysis capabilities of UCODE_2005. Simulated gradients in the surficial aquifer in the vicinity of the tidal Anacostia River indicate that flow is predominantly toward the river, with changes in the magnitude and direction of the gradients from the northeast, where the Anacostia River enters Washington, D.C., to the southwest, toward the confluence with the tidal Potomac River. Flow paths to the tidal Anacostia River from the north are largely horizontal through the surficial aquifer and Patuxent aquifer. From the south, the flow paths toward the river originate in the elevated topographic areas southeast of the river and pass through the surficial aquifer and Patapsco confining unit, lower Patapsco aquifer/Arundel Clay, and to some extent, the Patuxent aquifer. Groundwater-flow rates to and from the tidal rivers (Potomac and Anacostia) are generally greatest near the land-water boundary, where the gradient in the water table is greatest, and diminish toward the middle of the tidal river channels. The tidal rivers are predominantly areas of groundwater discharge, although there are areas where tidal waters are recharging the subsurface, typically where small variations or depressions in the topography produce small locally reversed gradients in the water table. Substantial recharge of tidal waters to the groundwater system is observed for the tidal Potomac where the upper Patapsco aquifer subcrops south of Washington, D.C. Water budget calculations indicate that inflows to the groundwater system beneath the tidal Anacostia River are predominantly from the land area of Washington, D.C., followed by tidal surface water and flows from lower layers. Outflows are largely to the tidal Anacostia River, with a smaller part going to the land area underlying Washington, D.C.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20135225","usgsCitation":"Raffensperger, J.P., Voronin, L.M., and Dieter, C.A., 2021, Simulation of groundwater flow in the aquifer system of the Anacostia River and surrounding watersheds, Washington, D.C., Maryland, and Virginia: U.S. Geological Survey Scientific Investigations Report 2013–5225, 59 p., https://doi.org/10.3133/sir20135225.","productDescription":"vii, 59 p.","numberOfPages":"59","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-051429","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":384505,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2013/5225/coverthb.jpg"},{"id":384506,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2013/5225/sir20135225.pdf","text":"Report","size":"8.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2013-5225"}],"country":"United States","state":"Delaware, Maryland, Washington D.C.","otherGeospatial":"Anacostia River and surrounding watersheds","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.70355224609375,\n 38.89958342598271\n ],\n [\n -76.44287109375,\n 38.1777509666256\n ],\n [\n -75.498046875,\n 39.14710270770074\n ],\n [\n -76.72027587890625,\n 39.715638134796336\n ],\n [\n -77.70355224609375,\n 38.89958342598271\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Maryland-Delaware-D.C. Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Catonsville, MD 21228